text: from machin.model.nets.base import static_module_wrapper as smw
  from machin.frame.algorithms.ars import ARS, RunningStat
  from machin.frame.helpers.servers import model_server_helper
  from machin.utils.helper_classes import Counter
  from machin.utils.conf import Config
  from machin.utils.learning_rate import gen_learning_rate_func
  from machin.env.utils.openai_gym import disable_view_window
  from torch.optim.lr_scheduler import LambdaLR
  
  import os
  import torch as t
  import torch.nn as nn
  import gym
  
  from .utils import unwrap_time_limit, Smooth
  from test.util_run_multi import *
  from test.util_fixtures import *
  
  
  class TestRunningStat:
      @pytest.mark.parametrize("shape", ((), (3,), (3, 4)))
      def test_push(self, shape):
          vals = []
          rs = RunningStat(shape)
          for _ in range(5):
              val = t.randn(shape, dtype=t.float64)
              rs.push(val)
              vals.append(val)
              m = t.mean(t.stack(vals), dim=0)
              assert t.allclose(rs.mean, m)
              v = (
                  t.square(m)
                  if (len(vals) == 1)
                  else t.var(t.stack(vals), dim=0, unbiased=True)
              )
              assert t.allclose(rs.var, v)
  
      @pytest.mark.parametrize("shape", ((), (3,), (3, 4)))
      def test_update(self, shape):
          rs1 = RunningStat(shape)
          rs2 = RunningStat(shape)
          rs = RunningStat(shape)
          for _ in range(5):
              val = t.randn(shape, dtype=t.float64)
              rs1.push(val)
              rs.push(val)
          for _ in range(9):
              val = t.randn(shape, dtype=t.float64)
              rs2.push(val)
              rs.push(val)
          rs1.update(rs2)
          assert t.allclose(rs.mean, rs1.mean)
          assert t.allclose(rs.std, rs1.std)
  
  
  # class ActorDiscrete(nn.Module):
  #     def __init__(self, state_dim, action_dim):
  #         super(ActorDiscrete, self).__init__()
  #
  #         self.fc1 = nn.Linear(state_dim, 16)
  #         self.fc2 = nn.Linear(16, action_dim)
  #
  #     def forward(self, state):
  #         a = self.fc1(state)
  #         a = t.argmax(self.fc2(a), dim=1).item()
  #         return a
  
  
  class ActorDiscrete(nn.Module):
      def __init__(self, state_dim, action_dim):
          super().__init__()
          self.fc = nn.Linear(state_dim, action_dim, bias=False)
  
      def forward(self, state):
          a = t.argmax(self.fc(state), dim=1).item()
          return a
  
  
  class TestARS:
      # configs and definitions
      # Cartpole-v0 can be solved:
      # within 200 episodes, using single layer Actor
      # within 400 episodes, using double layer Actor
  
      # However, ARS fails to deal with pendulum v0:
      # Actor((st, 16)->(16, a)), noise_std=0.01, lr=0.05, rollout=9, optim=Adam)
      # reaches mean score = -700 at 10000 episodes
      # Actor((st, a)), noise_std=0.01, lr=0.05, rollout=9, optim=Adam)
      # reaches mean score = -1100 at 15000 episodes
      # and Adam optimizer is better than SGD
      disable_view_window()
      c = Config()
      c.env_name = "CartPole-v0"
      c.env = unwrap_time_limit(gym.make(c.env_name))
      c.observe_dim = 4
      c.action_num = 2
      c.max_episodes = 1000
      c.max_steps = 200
      c.solved_reward = 150
      c.solved_repeat = 5
  
      @staticmethod
      def ars(device, dtype):
          c = TestARS.c
          actor = smw(
              ActorDiscrete(c.observe_dim, c.action_num).type(dtype).to(device),
              device,
              device,
          )
          servers = model_server_helper(model_num=1)
          world = get_world()
          ars_group = world.create_rpc_group("ars", ["0", "1", "2"])
          ars = ARS(
              actor,
              t.optim.SGD,
              ars_group,
              servers,
              noise_std_dev=0.1,
              learning_rate=0.1,
              noise_size=1000000,
              rollout_num=6,
              used_rollout_num=6,
              normalize_state=True,
          )
          return ars
  
      @staticmethod
      def ars_lr(device, dtype):
          c = TestARS.c
          actor = smw(
              ActorDiscrete(c.observe_dim, c.action_num).type(dtype).to(device),
              device,
              device,
          )
          lr_func = gen_learning_rate_func(
              [(0, 1e-3), (200000, 3e-4)], logger=default_logger
          )
          servers = model_server_helper(model_num=1)
          world = get_world()
          ars_group = world.create_rpc_group("ars", ["0", "1", "2"])
          ars = ARS(
              actor,
              t.optim.SGD,
              ars_group,
              servers,
              noise_size=1000000,
              lr_scheduler=LambdaLR,
              lr_scheduler_args=((lr_func,),),
          )
          return ars
  
      ########################################################################
      # Test for ARS acting
      ########################################################################
      @staticmethod
      @run_multi(
          expected_results=[True, True, True],
          pass_through=["device", "dtype"],
          timeout=180,
      )
      @WorldTestBase.setup_world
      def test_act(_, device, dtype):
          c = TestARS.c
          ars = TestARS.ars(device, dtype)
          state = t.zeros([1, c.observe_dim], dtype=dtype)
          ars.act({"state": state}, "original")
          ars.act({"state": state}, ars.get_actor_types()[0])
          with pytest.raises(ValueError):
              ars.act({"state": state}, "some_invalid_actor_type")
          return True
  
      ########################################################################
      # Test for ARS storage
      ########################################################################
      @staticmethod
      @run_multi(
          expected_results=[True, True, True],
          pass_through=["device", "dtype"],
          timeout=180,
      )
      @WorldTestBase.setup_world
      def test_store_reward(_, device, dtype):
          ars = TestARS.ars(device, dtype)
          ars.store_reward(0.0, ars.get_actor_types()[0])
          with pytest.raises(ValueError):
              ars.store_reward(1.0, "some_invalid_actor_type")
          return True
  
      ########################################################################
      # Test for ARS update
      ########################################################################
      @staticmethod
      @run_multi(
          expected_results=[True, True, True],
          pass_through=["device", "dtype"],
          timeout=180,
      )
      @WorldTestBase.setup_world
      def test_update(_, device, dtype):
          c = TestARS.c
          ars = TestARS.ars(device, dtype)
          for at in ars.get_actor_types():
              # get action will cause filters to initialize
              _action = ars.act({"state": t.zeros([1, c.observe_dim], dtype=dtype)}, at)
              if at.startswith("neg"):
                  ars.store_reward(1.0, at)
              else:
                  ars.store_reward(0.0, at)
          ars.update()
          return True
  
      ########################################################################
      # Test for ARS save & load
      ########################################################################
      # Skipped, it is the same as base
  
      ########################################################################
      # Test for ARS lr_scheduler
      ########################################################################
      @staticmethod
      @run_multi(
          expected_results=[True, True, True],
          pass_through=["device", "dtype"],
          timeout=180,
      )
      @WorldTestBase.setup_world
      def test_lr_scheduler(_, device, dtype):
          ars = TestARS.ars_lr(device, dtype)
          ars.update_lr_scheduler()
          return True
  
      ########################################################################
      # Test for ARS config & init
      ########################################################################
      @staticmethod
      @run_multi(expected_results=[True, True, True], timeout=180)
      @WorldTestBase.setup_world
      def test_config_init(_):
          c = TestARS.c
          config = ARS.generate_config({})
          config["frame_config"]["models"] = ["ActorDiscrete"]
          config["frame_config"]["model_kwargs"] = [
              {"state_dim": c.observe_dim, "action_dim": c.action_num}
          ]
          ars = ARS.init_from_config(config)
  
          for at in ars.get_actor_types():
              # get action will cause filters to initialize
              _action = ars.act(
                  {"state": t.zeros([1, c.observe_dim], dtype=t.float32)}, at
              )
              if at.startswith("neg"):
                  ars.store_reward(1.0, at)
              else:
                  ars.store_reward(0.0, at)
          ars.update()
          return True
  
      ########################################################################
      # Test for ARS full training.
      ########################################################################
      @staticmethod
      @run_multi(expected_results=[True, True, True], timeout=1800)
      @WorldTestBase.setup_world
      def test_full_train(rank):
          c = TestARS.c
          ars = TestARS.ars("cpu", t.float32)
  
          # begin training
          episode, step = Counter(), Counter()
          reward_fulfilled = Counter()
          smoother = Smooth()
          terminal = False
  
          env = c.env
          # for cpu usage viewing
          default_logger.info(f"{rank}, pid {os.getpid()}")
          while episode < c.max_episodes:
              episode.count()
  
              all_reward = 0
              for at in ars.get_actor_types():
                  total_reward = 0
  
                  # batch size = 1
                  state = t.tensor(env.reset(), dtype=t.float32)
                  while not terminal and step <= c.max_steps:
                      step.count()
                      with t.no_grad():
                          # agent model inference
                          action = ars.act({"state": state.unsqueeze(0)}, at)
                          state, reward, terminal, __ = env.step(action)
                          state = t.tensor(state, dtype=t.float32)
                          total_reward += float(reward)
                  step.reset()
                  terminal = False
                  ars.store_reward(total_reward, at)
                  all_reward += total_reward
  
              # update
              ars.update()
              smoother.update(all_reward / len(ars.get_actor_types()))
              default_logger.info(
                  f"Process {rank} Episode {episode} total reward={smoother.value:.2f}"
              )
  
              if smoother.value > c.solved_reward:
                  reward_fulfilled.count()
                  if reward_fulfilled >= c.solved_repeat:
                      default_logger.info("Environment solved!")
                      raise SafeExit
              else:
                  reward_fulfilled.reset()
  
          raise RuntimeError("ARS Training failed.")
  