




'''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      _UpperCamelCase	(_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Optional[int]				,		_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       Optional[int]				,		_lowerCamelCase							:       Any				,		_lowerCamelCase							:       Optional[int]		)->				Union[str, Any]:




     '''simple docstring'''





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

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

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

     __snake_case   ,      __snake_case   ,      __snake_case         =					model(
         states=SCREAMING_SNAKE_CASE_				,		actions=SCREAMING_SNAKE_CASE_				,		rewards=SCREAMING_SNAKE_CASE_				,		returns_to_go=SCREAMING_SNAKE_CASE_				,		timesteps=SCREAMING_SNAKE_CASE_				,		attention_mask=SCREAMING_SNAKE_CASE_				,		return_dict=SCREAMING_SNAKE_CASE_				,		)

     return action_preds[0, -1]


# build the environment

UpperCAmelCase_    :					List[str]					      =						gym.make('''Hopper-v3''')
UpperCAmelCase_    :					List[Any]					      =						env.observation_space.shape[0]
UpperCAmelCase_    :					List[Any]					      =						env.action_space.shape[0]
UpperCAmelCase_    :					int					      =						1_0_0_0
UpperCAmelCase_    :					Optional[Any]					      =						'''cuda'''
UpperCAmelCase_    :					Union[str, Any]					      =						1_0_0_0.0  # normalization for rewards/returns
UpperCAmelCase_    :					Dict					      =						3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCAmelCase_    :					str					      =						np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
UpperCAmelCase_    :					List[str]					      =						np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
UpperCAmelCase_    :					List[Any]					      =						torch.from_numpy(state_mean).to(device=device)
UpperCAmelCase_    :					Optional[Any]					      =						torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
     UpperCAmelCase_      ,   UpperCAmelCase_    :					Optional[int]					      =						0, 0
     UpperCAmelCase_    :					List[str]					      =						env.reset()
     UpperCAmelCase_    :					Optional[Any]					      =						torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
     UpperCAmelCase_    :					Dict					      =						torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
     UpperCAmelCase_    :					int					      =						torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
     UpperCAmelCase_    :					List[Any]					      =						torch.zeros(0, device=device, dtype=torch.floataa)

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

          UpperCAmelCase_    :					Optional[int]					      =						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_    :					Optional[int]					      =						action
          UpperCAmelCase_    :					Dict					      =						action.detach().cpu().numpy()

          UpperCAmelCase_      ,   UpperCAmelCase_      ,   UpperCAmelCase_      ,   UpperCAmelCase_    :					int					      =						env.step(action)

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

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









'''simple docstring'''



from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					data
            __snake_case         =					None




       def __str__(				self					) ->					str:



            '''simple docstring'''




            return F'''{self.data}'''





class       lowerCAmelCase				:


       def __init__(				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					None
            __snake_case         =					None


       def __iter__(				self					) ->					Iterator[Any]:



            '''simple docstring'''




            __snake_case         =					self.front
            while node:
                 yield node.data
                 __snake_case         =					node.next


       def __len__(				self					) ->					int:



            '''simple docstring'''




            return len(tuple(iter(self					)					)					)


       def __str__(				self					) ->					str:



            '''simple docstring'''




            return " <- ".join(str(__SCREAMING_SNAKE_CASE					) for item in self					)


       def  lowerCAmelCase (				self					) ->					bool:



            '''simple docstring'''




            return len(self					) == 0


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					Node(__SCREAMING_SNAKE_CASE					)
            if self.is_empty():
                 __snake_case         =					__snake_case         =					node
            else:
                 assert isinstance(self.rear      ,					__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					node
                 __snake_case         =					node


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            if self.is_empty():
                 raise IndexError('''dequeue from empty queue'''					)
            assert isinstance(self.front      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.front
            __snake_case         =					self.front.next
            if self.front is None:
                 __snake_case         =					None
            return node.data




       def  lowerCAmelCase (				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					__snake_case         =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()






