



"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class       UpperCamelCase  :





     """simple docstring"""






     _lowerCamelCase					: Dict				   =("_front", "_back", "_len")

     @dataclass
     class       UpperCamelCase  :





          """simple docstring"""






          _lowerCamelCase					: Any				   =None
          _lowerCamelCase					: Deque._Node | None				   =None
          _lowerCamelCase					: Deque._Node | None				   =None


     class       UpperCamelCase  :





          """simple docstring"""






          _lowerCamelCase					: Optional[Any]				   =("_cur",)





          def __init__(					self :							Union[str, Any]	,      _lowerCamelCase :							Deque._Node | None ):
               A__				=   cur





          def __iter__(					self :							Tuple ):
               return self





          def 						A__  (					self :							List[str] ):
               if self._cur is None:
                    # finished iterating
                    raise StopIteration
               A__				=   self._cur.val
               A__				=   self._cur.next_node

               return val





     def __init__(					self :							List[str]	,      _lowerCamelCase :							Iterable[Any] | None = None ):
          A__				=   None
          A__				=   None
          A__				=   0

          if iterable is not None:
               # append every value to the deque
               for val in iterable:
                    self.append(_lowerCamelCase )





     def 						A__  (					self :							Union[str, Any]	,      _lowerCamelCase :							Any ):
          A__				=   self._Node(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )
          if self.is_empty():
               # front = back
               A__				=   A__				=   node
               A__				=   1
          else:
               # connect nodes
               A__				=   node
               A__				=   self._back
               A__				=   node  # assign new back to the new node

               self._len += 1

               # make sure there were no errors
               assert not self.is_empty(), "Error on appending value."





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							Any ):
          A__				=   self._Node(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )
          if self.is_empty():
               # front = back
               A__				=   A__				=   node
               A__				=   1
          else:
               # connect nodes
               A__				=   self._front
               A__				=   node
               A__				=   node  # assign new front to the new node

               self._len += 1

               # make sure there were no errors
               assert not self.is_empty(), "Error on appending value."





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Iterable[Any] ):
          for val in iterable:
               self.append(_lowerCamelCase )





     def 						A__  (					self :							str	,      _lowerCamelCase :							Iterable[Any] ):
          for val in iterable:
               self.appendleft(_lowerCamelCase )





     def 						A__  (					self :							List[Any] ):
          assert not self.is_empty(), "Deque is empty."

          A__				=   self._back
          A__				=   self._back.prev_node  # set new back
          # drop the last node - python will deallocate memory automatically
          A__				=   None

          self._len -= 1

          return topop.val





     def 						A__  (					self :							Dict ):
          assert not self.is_empty(), "Deque is empty."

          A__				=   self._front
          A__				=   self._front.next_node  # set new front and drop the first node
          A__				=   None

          self._len -= 1

          return topop.val





     def 						A__  (					self :							List[str] ):
          return self._front is None





     def __len__(					self :							str ):
          return self._len





     def __eq__(					self :							str	,      _lowerCamelCase :							object ):

          if not isinstance(_lowerCamelCase	,      _lowerCamelCase ):
               return NotImplemented

          A__				=   self._front
          A__				=   other._front

          # if the length of the dequeues are not the same, they are not equal
          if len(self ) != len(_lowerCamelCase ):
               return False

          while me is not None and oth is not None:
               # compare every value
               if me.val != oth.val:
                    return False
               A__				=   me.next_node
               A__				=   oth.next_node

          return True





     def __iter__(					self :							Any ):
          return Deque._Iterator(self._front )





     def __repr__(					self :							Optional[Any] ):
          A__				=   []
          A__				=   self._front
          while aux is not None:
               # append the values in a list to display
               values_list.append(aux.val )
               A__				=   aux.next_node

          return F'''[{', '.join(repr(_lowerCamelCase ) for val in values_list )}]'''


if __name__ == "__main__":
 import doctest

 doctest.testmod()








"""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   a_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a      ):
     # we don't care about the past rewards in this model

     A__				=   states.reshape(1		,							-1		,							model.config.state_dim      )
     A__				=   actions.reshape(1		,							-1		,							model.config.act_dim      )
     A__				=   returns_to_go.reshape(1		,							-1		,							1      )
     A__				=   timesteps.reshape(1		,							-1      )

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

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

     A__	,     A__	,     A__				=   model(
         states=__a		,							actions=__a		,							rewards=__a		,							returns_to_go=__a		,							timesteps=__a		,							attention_mask=__a		,							return_dict=__a		,							)

     return action_preds[0, -1]


# build the environment

__snake_case	:				List[str]  					=			gym.make('Hopper-v3')
__snake_case	:				Optional[Any]  					=			env.observation_space.shape[0]
__snake_case	:				List[str]  					=			env.action_space.shape[0]
__snake_case	:				Optional[int]  					=			1_000
__snake_case	:				Any  					=			'cuda'
__snake_case	:				str  					=			1_000.0  # normalization for rewards/returns
__snake_case	:				str  					=			3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case	:				int  					=			np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
__snake_case	:				str  					=			np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
__snake_case	:				str  					=			torch.from_numpy(state_mean).to(device=device)
__snake_case	:				str  					=			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__snake_case	:				str  					=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
__snake_case	:				str  					=			model.to(device)
model.eval()

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

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

  __snake_case	:				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),
  )
  __snake_case	:				List[str]  					=			action
  __snake_case	:				int  					=			action.detach().cpu().numpy()

  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case	:				Union[str, Any]  					=			env.step(action)

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

  __snake_case	:				Union[str, Any]  					=			target_return[0, -1] - (reward / scale)
  __snake_case	:				Dict  					=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __snake_case	:				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



