'''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 				lowerCamelCase      ( UpperCAmelCase__			:   Union[str, Any] , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   Optional[int] , UpperCAmelCase__			:   Any , UpperCAmelCase__			:   Optional[int]					)     -> Union[str, Any]:
							# we don't care about the past rewards in this model

							lowercase_   :     Dict									=			states.reshape(1 , -1 , model.config.state_dim					)
							lowercase_   :     Any									=			actions.reshape(1 , -1 , model.config.act_dim					)
							lowercase_   :     int									=			returns_to_go.reshape(1 , -1 , 1					)
							lowercase_   :     Tuple									=			timesteps.reshape(1 , -1					)

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

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

							lowercase_   :     str									=			model(
							    states=snake_case_ , actions=snake_case_ , rewards=snake_case_ , returns_to_go=snake_case_ , timesteps=snake_case_ , attention_mask=snake_case_ , return_dict=snake_case_ , )

							return action_preds[0, -1]


# build the environment

_lowercase		:					Dict													=		gym.make("Hopper-v3")
_lowercase		:					List[str]													=		env.observation_space.shape[0]
_lowercase		:					Optional[Any]													=		env.action_space.shape[0]
_lowercase		:					List[Any]													=		1000
_lowercase		:					Union[str, Any]													=		"cuda"
_lowercase		:					Any													=		1000.0  # normalization for rewards/returns
_lowercase		:					Optional[Any]													=		3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowercase		:					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,
    ]
)
_lowercase		:					Optional[int]													=		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,
    ]
)
_lowercase		:					Dict													=		torch.from_numpy(state_mean).to(device=device)
_lowercase		:					Union[str, Any]													=		torch.from_numpy(state_std).to(device=device)

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

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

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

												_lowercase		:					List[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),
												)
												_lowercase		:					Tuple													=		action
												_lowercase		:					List[str]													=		action.detach().cpu().numpy()

												_lowercase							,			_lowercase							,			_lowercase							,			_lowercase		:					Dict													=		env.step(action)

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

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








from __future__ import annotations





def  _snake_case		(snake_case_					:     str  )							->		bool:
  __UpperCAmelCase	:  int
  __UpperCAmelCase	:  list[str]		=     list(snake_case_  )
  __UpperCAmelCase,			__UpperCAmelCase	:  Any		=     _vector[:-1], int(_vector[-1]  )
  __UpperCAmelCase	:  list[int]		=     [int(snake_case_  ) for digit in __vector]

  vector.reverse()
  for i, digit in enumerate(snake_case_  ):
    if i & 1 == 0:
      __UpperCAmelCase	:  int		=     digit * 2
      if doubled > 9:
        doubled -= 9
      check_digit += doubled
    else:
      check_digit += digit

  return check_digit % 10 == 0


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      assert is_luhn('''79927398713''')
      assert not is_luhn('''79927398714''')





