






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

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

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

   A_      ,						A_      ,						A_					:				Union[str, Any]          =			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

_lowerCAmelCase							:      Tuple						=	gym.make('''Hopper-v3''')
_lowerCAmelCase							:      int						=	env.observation_space.shape[0]
_lowerCAmelCase							:      str						=	env.action_space.shape[0]
_lowerCAmelCase							:      List[Any]						=	1_000
_lowerCAmelCase							:      Tuple						=	'''cuda'''
_lowerCAmelCase							:      int						=	1_000.0  # normalization for rewards/returns
_lowerCAmelCase							:      Optional[int]						=	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase							:      int						=	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,
    ]
)
_lowerCAmelCase							:      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,
    ]
)
_lowerCAmelCase							:      Optional[int]						=	torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase							:      Any						=	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_lowerCAmelCase							:      Dict						=	DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
_lowerCAmelCase							:      Optional[Any]						=	model.to(device)
model.eval()

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

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

  _lowerCAmelCase							:      Dict						=	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),
  )
  _lowerCAmelCase							:      Dict						=	action
  _lowerCAmelCase							:      str						=	action.detach().cpu().numpy()

  _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase							:      Optional[int]						=	env.step(action)

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

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







def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						float:
   A_					:				List[str]          =			[1 / first_term]
   A_					:				Tuple          =			1 / first_term
   for _ in range(number_of_terms - 1	):
      first_term += common_difference
      arithmetic_progression.append(_lowerCAmelCase	)
   A_					:				List[str]          =			[1 / step for step in arithmetic_progression]
   return sum(_lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))
