


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 							UpperCamelCase_  (				__snake_case			:						Tuple	,			__snake_case			:						List[Any]	,			__snake_case			:						List[str]	,			__snake_case			:						Union[str, Any]	,			__snake_case			:						Optional[Any]	,			__snake_case			:						Any     )->      Optional[Any]:




						'''simple docstring'''
						__magic_name__ 		=			states.reshape(1	,			-1	,			model.config.state_dim     )
						__magic_name__ 		=			actions.reshape(1	,			-1	,			model.config.act_dim     )
						__magic_name__ 		=			returns_to_go.reshape(1	,			-1	,			1     )
						__magic_name__ 		=			timesteps.reshape(1	,			-1     )

						if model.config.max_length is not None:
												__magic_name__ 		=			states[:, -model.config.max_length :]
												__magic_name__ 		=			actions[:, -model.config.max_length :]
												__magic_name__ 		=			returns_to_go[:, -model.config.max_length :]
												__magic_name__ 		=			timesteps[:, -model.config.max_length :]

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

						__magic_name__		,			__magic_name__		,			__magic_name__ 		=			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 :				str						    =       gym.make('''Hopper-v3''')
UpperCamelCase :				List[Any]						    =       env.observation_space.shape[0]
UpperCamelCase :				int						    =       env.action_space.shape[0]
UpperCamelCase :				List[Any]						    =       1_000
UpperCamelCase :				Tuple						    =       '''cuda'''
UpperCamelCase :				Optional[int]						    =       1_000.0  # normalization for rewards/returns
UpperCamelCase :				Dict						    =       3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase :				List[str]						    =       np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
UpperCamelCase :				Union[str, Any]						    =       np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
UpperCamelCase :				List[Any]						    =       torch.from_numpy(state_mean).to(device=device)
UpperCamelCase :				List[Any]						    =       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
UpperCamelCase :				Optional[int]						    =       DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
UpperCamelCase :				Union[str, Any]						    =       model.to(device)
model.eval()

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

					UpperCamelCase :				str						    =       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 :				str						    =       torch.cat([rewards, torch.zeros(1, device=device)])

										UpperCamelCase :				Optional[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),
										)
										UpperCamelCase :				Union[str, Any]						    =       action
										UpperCamelCase :				Dict						    =       action.detach().cpu().numpy()

										UpperCamelCase    ,UpperCamelCase    ,UpperCamelCase    ,UpperCamelCase :				List[Any]						    =       env.step(action)

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

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





def 							UpperCamelCase_  (				__snake_case			:						int     )->      bool:




						'''simple docstring'''
						if not isinstance(__snake_case	,			__snake_case     ):
												__magic_name__ 		=			F'''Input value of [number={number}] must be an integer'''
												raise TypeError(__snake_case     )
						if number < 0 or number % 2 == 1:
												return False
						__magic_name__ 		=			int(number**0.5     )
						return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

