





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__(		lowercase    :      Union[str, Any]				,       lowercase    :      Optional[Any]				,       lowercase    :      Dict				,       lowercase    :      List[Any]				,       lowercase    :      Optional[int]				,       lowercase    :      List[Any]			)	->      int:
  # we don't care about the past rewards in this model

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

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

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

  __snake_case      ,				__snake_case      ,				__snake_case				:			Optional[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

_UpperCamelCase    =	gym.make('''Hopper-v3''')
_UpperCamelCase    =	env.observation_space.shape[0]
_UpperCamelCase    =	env.action_space.shape[0]
_UpperCamelCase    =	1000
_UpperCamelCase    =	'''cuda'''
_UpperCamelCase    =	10_00.0  # normalization for rewards/returns
_UpperCamelCase    =	3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase    =	np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_UpperCamelCase    =	np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_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








_UpperCamelCase    =	[chr(i) for i in range(32, 126)]
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(reversed(range(len(alphabets))))
_UpperCamelCase    =	[]
_UpperCamelCase    =	_UpperCamelCase    =	_UpperCamelCase    =	0







def  lowerCAmelCase__(		)	->      int:
  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __snake_case				:			int            =    gear_one[0]
  gear_one.append(lowercase			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(lowercase			)			) == 0:
    __snake_case				:			List[str]            =    gear_two[0]
    gear_two.append(lowercase			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(lowercase			)			) == 0:
      __snake_case				:			Any            =    gear_three[0]
      gear_three.append(lowercase			)
      del gear_three[0]
      gear_three_pos += 1







def  lowerCAmelCase__(		lowercase    :      Tuple			)	->      Union[str, Any]:
  __snake_case				:			Union[str, Any]            =    alphabets.index(lowercase			)
  __snake_case				:			Tuple            =    gear_one[target]
  __snake_case				:			Union[str, Any]            =    gear_two[target]
  __snake_case				:			int            =    gear_three[target]
  __snake_case				:			Optional[int]            =    reflector[target]
  __snake_case				:			Union[str, Any]            =    gear_three.index(lowercase			)
  __snake_case				:			Optional[Any]            =    gear_two.index(lowercase			)
  __snake_case				:			int            =    gear_one.index(lowercase			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     _UpperCamelCase    =	list(input('''Type your message:\n'''))
     while True:
          try:
               _UpperCamelCase    =	int(input('''Please set token:(must be only digits)\n'''))
               break
          except Exception as error:
               print(error)
     for _ in range(token):
          rotator()
     for j in decode:
          engine(j)
     print('''\n''' + ''''''.join(code))
     print(
         F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
         '''this message again you should input same digits as token!'''
     )

