





"""simple docstring"""




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  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Union[str, Any]						     =					states.reshape(1 ,		-1 ,		model.config.state_dim     )
						_UpperCamelCase    :      List[Any]						     =					actions.reshape(1 ,		-1 ,		model.config.act_dim     )
						_UpperCamelCase    :      List[Any]						     =					returns_to_go.reshape(1 ,		-1 ,		1     )
						_UpperCamelCase    :      List[Any]						     =					timesteps.reshape(1 ,		-1     )

						if model.config.max_length is not None:
												_UpperCamelCase    :      List[Any]						     =					states[:, -model.config.max_length :]
												_UpperCamelCase    :      Any						     =					actions[:, -model.config.max_length :]
												_UpperCamelCase    :      Dict						     =					returns_to_go[:, -model.config.max_length :]
												_UpperCamelCase    :      Dict						     =					timesteps[:, -model.config.max_length :]

												# pad all tokens to sequence length
												_UpperCamelCase    :      Union[str, Any]						     =					torch.cat(
												    [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
												_UpperCamelCase    :      int						     =					attention_mask.to(dtype=torch.long ,		device=states.device     ).reshape(1 ,		-1     )
												_UpperCamelCase    :      int						     =					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     )
												_UpperCamelCase    :      List[str]						     =					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     )
												_UpperCamelCase    :      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     )
												_UpperCamelCase    :      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:
												_UpperCamelCase    :      Optional[Any]						     =					None

						_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase    :      int						     =					model(
						    states=lowercase__ ,		actions=lowercase__ ,		rewards=lowercase__ ,		returns_to_go=lowercase__ ,		timesteps=lowercase__ ,		attention_mask=lowercase__ ,		return_dict=lowercase__ ,		)

						return action_preds[0, -1]


# build the environment

UpperCamelCase_     =gym.make("""Hopper-v3""")
UpperCamelCase_     =env.observation_space.shape[0]
UpperCamelCase_     =env.action_space.shape[0]
UpperCamelCase_     =1_000
UpperCamelCase_     ="""cuda"""
UpperCamelCase_     =1_000.0  # normalization for rewards/returns
UpperCamelCase_     =3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase_     =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_     =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_     =torch.from_numpy(state_mean).to(device=device)
UpperCamelCase_     =torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
						UpperCamelCase_   ,			UpperCamelCase_     =0, 0
						UpperCamelCase_     =env.reset()
						UpperCamelCase_     =torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						UpperCamelCase_     =torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
						UpperCamelCase_     =torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
						UpperCamelCase_     =torch.zeros(0, device=device, dtype=torch.floataa)

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

												UpperCamelCase_     =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_     =action
												UpperCamelCase_     =action.detach().cpu().numpy()

												UpperCamelCase_   ,			UpperCamelCase_   ,			UpperCamelCase_   ,			UpperCamelCase_     =env.step(action)

												UpperCamelCase_     =torch.from_numpy(state).to(device=device).reshape(1, state_dim)
												UpperCamelCase_     =torch.cat([states, cur_state], dim=0)
												UpperCamelCase_     =reward

												UpperCamelCase_     =target_return[0, -1] - (reward / scale)
												UpperCamelCase_     =torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												UpperCamelCase_     =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










"""simple docstring"""




def  a_       (							_lowercase     ):
						if len(_lowercase     ) > 1:
												_UpperCamelCase    :      List[str]						     =					len(_lowercase     ) // 2  # Finds the middle of the array
												_UpperCamelCase    :      Dict						     =					arr[
												    :middle_length
												]  # Creates an array of the elements in the first half.
												_UpperCamelCase    :      Union[str, Any]						     =					arr[
												    middle_length:
												]  # Creates an array of the elements in the second half.
												_UpperCamelCase    :      str						     =					len(_lowercase     )
												_UpperCamelCase    :      List[str]						     =					len(_lowercase     )
												merge(_lowercase     )  # Starts sorting the left.
												merge(_lowercase     )  # Starts sorting the right
												_UpperCamelCase    :      Dict						     =					0  # Left Counter
												_UpperCamelCase    :      Union[str, Any]						     =					0  # Right Counter
												_UpperCamelCase    :      Optional[int]						     =					0  # Position Counter
												while (
												    left_index < left_size and right_index < right_size
												):  # Runs until the lowers size of the left and right are sorted.
																		if left_array[left_index] < right_array[right_index]:
																								_UpperCamelCase    :      Dict						     =					left_array[left_index]
																								left_index += 1
																		else:
																								_UpperCamelCase    :      Tuple						     =					right_array[right_index]
																								right_index += 1
																		index += 1
												while (
												    left_index < left_size
												):  # Adds the left over elements in the left half of the array
																		_UpperCamelCase    :      int						     =					left_array[left_index]
																		left_index += 1
																		index += 1
												while (
												    right_index < right_size
												):  # Adds the left over elements in the right half of the array
																		_UpperCamelCase    :      Any						     =					right_array[right_index]
																		right_index += 1
																		index += 1
						return arr


if __name__ == "__main__":
						import doctest

						doctest.testmod()



