


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       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)->			List[Any]:
				# we don't care about the past rewards in this model

				lowerCAmelCase_			:   int     					=					states.reshape(1			, -1			, model.config.state_dim			)
				lowerCAmelCase_			:   Optional[Any]     					=					actions.reshape(1			, -1			, model.config.act_dim			)
				lowerCAmelCase_			:   int     					=					returns_to_go.reshape(1			, -1			, 1			)
				lowerCAmelCase_			:   List[str]     					=					timesteps.reshape(1			, -1			)

				if model.config.max_length is not None:
								lowerCAmelCase_			:   Union[str, Any]     					=					states[:, -model.config.max_length :]
								lowerCAmelCase_			:   Optional[int]     					=					actions[:, -model.config.max_length :]
								lowerCAmelCase_			:   Tuple     					=					returns_to_go[:, -model.config.max_length :]
								lowerCAmelCase_			:   Union[str, Any]     					=					timesteps[:, -model.config.max_length :]

								# pad all tokens to sequence length
								lowerCAmelCase_			:   List[Any]     					=					torch.cat(
								    [torch.zeros(model.config.max_length - states.shape[1]			), torch.ones(states.shape[1]			)]			)
								lowerCAmelCase_			:   int     					=					attention_mask.to(dtype=torch.long			, device=states.device			).reshape(1			, -1			)
								lowerCAmelCase_			:   Union[str, 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_			:   Tuple     					=					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_			:   Tuple     					=					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_			:   Optional[Any]     					=					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_			:   Dict     					=					model(
				    states=lowerCAmelCase_			, actions=lowerCAmelCase_			, rewards=lowerCAmelCase_			, returns_to_go=lowerCAmelCase_			, timesteps=lowerCAmelCase_			, attention_mask=lowerCAmelCase_			, return_dict=lowerCAmelCase_			, )

				return action_preds[0, -1]


# build the environment

_UpperCAmelCase      :	int  =gym.make("""Hopper-v3""")
_UpperCAmelCase      :	Dict  =env.observation_space.shape[0]
_UpperCAmelCase      :	Tuple  =env.action_space.shape[0]
_UpperCAmelCase      :	Optional[Any]  =1000
_UpperCAmelCase      :	Optional[int]  ="""cuda"""
_UpperCAmelCase      :	Optional[int]  =1000.0  # normalization for rewards/returns
_UpperCAmelCase      :	Tuple  =3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCAmelCase      :	List[Any]  =np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_UpperCAmelCase      :	Optional[int]  =np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_UpperCAmelCase      :	int  =torch.from_numpy(state_mean).to(device=device)
_UpperCAmelCase      :	Dict  =torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_UpperCAmelCase      :	str  =DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
_UpperCAmelCase      :	int  =model.to(device)
model.eval()

for ep in range(10):
						_UpperCAmelCase  ,       _UpperCAmelCase      :	int  =0, 0
						_UpperCAmelCase      :	Optional[Any]  =env.reset()
						_UpperCAmelCase      :	List[Any]  =torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						_UpperCAmelCase      :	List[str]  =torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
						_UpperCAmelCase      :	Union[str, Any]  =torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
						_UpperCAmelCase      :	Optional[int]  =torch.zeros(0, device=device, dtype=torch.floataa)

						_UpperCAmelCase      :	Tuple  =torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
						for t in range(max_ep_len):
												env.render()
												# add padding
												_UpperCAmelCase      :	int  =torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
												_UpperCAmelCase      :	Optional[int]  =torch.cat([rewards, torch.zeros(1, device=device)])

												_UpperCAmelCase      :	str  =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),
												)
												_UpperCAmelCase      :	List[Any]  =action
												_UpperCAmelCase      :	Dict  =action.detach().cpu().numpy()

												_UpperCAmelCase  ,       _UpperCAmelCase  ,       _UpperCAmelCase  ,       _UpperCAmelCase      :	str  =env.step(action)

												_UpperCAmelCase      :	Dict  =torch.from_numpy(state).to(device=device).reshape(1, state_dim)
												_UpperCAmelCase      :	Tuple  =torch.cat([states, cur_state], dim=0)
												_UpperCAmelCase      :	Union[str, Any]  =reward

												_UpperCAmelCase      :	int  =target_return[0, -1] - (reward / scale)
												_UpperCAmelCase      :	Tuple  =torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												_UpperCAmelCase      :	Union[str, 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



from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
_UpperCAmelCase      :	int  =[[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       lowerCAmelCase			(		lowerCAmelCase_			)->			list[int]:
				return [int(lowerCAmelCase_			) for c in f"""{ruleset:08}"""[:8]]



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)->			list[int]:
				lowerCAmelCase_			:   Optional[Any]     					=					len(cells[0]			)  # 31
				lowerCAmelCase_			:   int     					=					[]
				for i in range(lowerCAmelCase_			):
								# Get the neighbors of each cell
								# Handle neighbours outside bounds by using 0 as their value
								lowerCAmelCase_			:   Union[str, Any]     					=					0 if i == 0 else cells[time][i - 1]
								lowerCAmelCase_			:   List[str]     					=					0 if i == population - 1 else cells[time][i + 1]
								# Define a new cell and add it to the new generation
								lowerCAmelCase_			:   List[Any]     					=					7 - int(f"""{left_neighbor}{cells[time][i]}{right_neighbor}"""			, 2			)
								next_generation.append(rule[situation]			)
				return next_generation



def       lowerCAmelCase			(		lowerCAmelCase_			)->			Image.Image:
				lowerCAmelCase_			:   Optional[Any]     					=					Image.new('''RGB'''			, (len(cells[0]			), len(lowerCAmelCase_			))			)
				lowerCAmelCase_			:   str     					=					img.load()
				# Generates image
				for w in range(img.width			):
								for h in range(img.height			):
												lowerCAmelCase_			:   Optional[Any]     					=					255 - int(255 * cells[h][w]			)
												lowerCAmelCase_			:   List[Any]     					=					(color, color, color)
				return img


if __name__ == "__main__":
						_UpperCAmelCase      :	str  =bin(int(input("""Rule:\n""").strip()))[2:]
						_UpperCAmelCase      :	List[str]  =format_ruleset(int(rule_num))
						for time in range(16):
												CELLS.append(new_generation(CELLS, rule, time))
						_UpperCAmelCase      :	Dict  =generate_image(CELLS)
						# Uncomment to save the image
						# img.save(f"rule_{rule_num}.png")
						img.show()