code: 
  
  from __future__ import annotations
  
  from PIL import Image
  
  # Define the first generation of cells
  # fmt: off
  __A   :     str      =			[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> list[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      return [int(_UpperCAmelCase				) for c in f"{ruleset:08}"[:8]]
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Union[str, Any]    			=      len(cells[0]				)  # 31
      lowerCAmelCase				:			Optional[Any]    			=      []
      for i in range(_UpperCAmelCase				):
          # Get the neighbors of each cell
          # Handle neighbours outside bounds by using 0 as their value
          lowerCAmelCase				:			List[str]    			=      0 if i == 0 else cells[time][i - 1]
          lowerCAmelCase				:			int    			=      0 if i == population - 1 else cells[time][i + 1]
          # Define a new cell and add it to the new generation
          lowerCAmelCase				:			Any    			=      7 - int(f"{left_neighbor}{cells[time][i]}{right_neighbor}",      2				)
          next_generation.append(rule[situation]				)
      return next_generation
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> Image.Image:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Dict    			=      Image.new('RGB',      (len(cells[0]				), len(_UpperCAmelCase				))				)
      lowerCAmelCase				:			Dict    			=      img.load()
      # Generates image
      for w in range(img.width				):
          for h in range(img.height				):
              lowerCAmelCase				:			Union[str, Any]    			=      255 - int(255 * cells[h][w]				)
              lowerCAmelCase				:			Optional[Any]    			=      (color, color, color)
      return img
  
  
  if __name__ == "__main__":
   __A   :     Any      =			bin(int(input('''Rule:\n''').strip()))[2:]
   __A   :     Dict      =			format_ruleset(int(rule_num))
   for time in range(16):
    CELLS.append(new_generation(CELLS, rule, time))
   __A   :     Optional[Any]      =			generate_image(CELLS)
   # Uncomment to save the image
   # img.save(f"rule_{rule_num}.png")
   img.show()
  
  
  
  
  
  
style_context: 
  
  import gym
  import numpy as np
  import torch
  from mujoco_py import GlfwContext
  
  from transformers import DecisionTransformerModel
  
  
  GlfwContext(offscreen=True)  # Create a window to init GLFW.
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Optional[Any]    			=      states.reshape(1,      -1,      model.config.state_dim				)
      lowerCAmelCase				:			List[Any]    			=      actions.reshape(1,      -1,      model.config.act_dim				)
      lowerCAmelCase				:			List[Any]    			=      returns_to_go.reshape(1,      -1,      1				)
      lowerCAmelCase				:			Dict    			=      timesteps.reshape(1,      -1				)
  
      if model.config.max_length is not None:
          lowerCAmelCase				:			Optional[Any]    			=      states[:, -model.config.max_length :]
          lowerCAmelCase				:			Union[str, Any]    			=      actions[:, -model.config.max_length :]
          lowerCAmelCase				:			Dict    			=      returns_to_go[:, -model.config.max_length :]
          lowerCAmelCase				:			List[str]    			=      timesteps[:, -model.config.max_length :]
  
          # pad all tokens to sequence length
          lowerCAmelCase				:			List[str]    			=      torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]				), torch.ones(states.shape[1]				)]				)
          lowerCAmelCase				:			List[Any]    			=      attention_mask.to(dtype=torch.long,      device=states.device				).reshape(1,      -1				)
          lowerCAmelCase				:			Any    			=      torch.cat(
              [
                  torch.zeros(
                      (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),      device=states.device,      ),
                  states,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			int    			=      torch.cat(
              [
                  torch.zeros(
                      (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),      device=actions.device,      ),
                  actions,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			Union[str, Any]    			=      torch.cat(
              [
                  torch.zeros(
                      (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),      device=returns_to_go.device,      ),
                  returns_to_go,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			Tuple    			=      torch.cat(
              [
                  torch.zeros(
                      (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),      device=timesteps.device				),
                  timesteps,
              ],      dim=1,      ).to(dtype=torch.long				)
      else:
          lowerCAmelCase				:			Optional[Any]    			=      None
  
      lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      model(
          states=_UpperCAmelCase,      actions=_UpperCAmelCase,      rewards=_UpperCAmelCase,      returns_to_go=_UpperCAmelCase,      timesteps=_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      return_dict=_UpperCAmelCase,      )
  
      return action_preds[0, -1]
  
  
  # build the environment
  
  __A   :     Dict      =			gym.make('''Hopper-v3''')
  __A   :     Any      =			env.observation_space.shape[0]
  __A   :     Optional[Any]      =			env.action_space.shape[0]
  __A   :     Any      =			1000
  __A   :     Tuple      =			'''cuda'''
  __A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
  __A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __A   :     str      =			np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  __A   :     Dict      =			np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  __A   :     int      =			torch.from_numpy(state_mean).to(device=device)
  __A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __A   :     List[Any]      =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
  __A   :     Any      =			model.to(device)
  model.eval()
  
  for ep in range(10):
   __A     ,      __A   :     Optional[int]      =			0, 0
   __A   :     List[str]      =			env.reset()
   __A   :     Optional[int]      =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   __A   :     str      =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   __A   :     Optional[int]      =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   __A   :     Any      =			torch.zeros(0, device=device, dtype=torch.floataa)
  
   __A   :     List[Any]      =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    __A   :     Union[str, Any]      =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    __A   :     List[Any]      =			torch.cat([rewards, torch.zeros(1, device=device)])
  
    __A   :     int      =			get_action(
        model,
        (states.to(dtype=torch.floataa) - state_mean) / state_std,
        actions.to(dtype=torch.floataa),
        rewards.to(dtype=torch.floataa),
        target_return.to(dtype=torch.floataa),
        timesteps.to(dtype=torch.long),
    )
    __A   :     List[str]      =			action
    __A   :     List[str]      =			action.detach().cpu().numpy()
  
    __A     ,      __A     ,      __A     ,      __A   :     List[str]      =			env.step(action)
  
    __A   :     Union[str, Any]      =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    __A   :     Any      =			torch.cat([states, cur_state], dim=0)
    __A   :     Dict      =			reward
  
    __A   :     str      =			target_return[0, -1] - (reward / scale)
    __A   :     Dict      =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    __A   :     List[Any]      =			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)
  
    episode_return += reward
    episode_length += 1
  
    if done:
     break
  
  
  
  
  
  