

'''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 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







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

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

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

					UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase		:							List[str]        =       model(
					    states=__magic_name__			,   actions=__magic_name__			,   rewards=__magic_name__			,   returns_to_go=__magic_name__			,   timesteps=__magic_name__			,   attention_mask=__magic_name__			,   return_dict=__magic_name__			,   )

					return action_preds[0, -1]


# build the environment

a       :      Any						=			gym.make("Hopper-v3")
a       :      Optional[int]						=			env.observation_space.shape[0]
a       :      List[Any]						=			env.action_space.shape[0]
a       :      List[str]						=			10_00
a       :      List[Any]						=			'cuda'
a       :      Any						=			10_00.0  # normalization for rewards/returns
a       :      List[str]						=			36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a       :      int						=			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,
    ]
)
a       :      Tuple						=			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,
    ]
)
a       :      str						=			torch.from_numpy(state_mean).to(device=device)
a       :      Optional[int]						=			torch.from_numpy(state_std).to(device=device)

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

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

								a       :      List[Any]						=			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       :      Union[str, Any]						=			action
								a       :      int						=			action.detach().cpu().numpy()

								a       :      Any						=			env.step(action)

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

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



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       1.0
					for i in range(1			,   r + 1							):
										ret *= (n + 1 - i) / i
					return round(__magic_name__							)





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					return choose(8 + n			,   __magic_name__							) + choose(9 + n			,   __magic_name__							) - 10





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					return sum(non_bouncy_exact(__magic_name__							) for i in range(1			,   n + 1							)							)





def 				lowercase (					__magic_name__ = 100							):



					'''simple docstring'''







					return non_bouncy_upto(__magic_name__							)


if __name__ == "__main__":
				print(F'{solution() = }')
