


'''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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Tuple   )		->					str:





   '''simple docstring'''







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

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

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

   UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						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

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

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

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

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

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

      __lowercase:		Optional[Any]							=     env.step(action)

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

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

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__lowercase:		Optional[Any]							=     TypeVar("T", bound=bool)







class  UpperCAmelCase (       Generic[T]):

 def __init__( self :	int,					a_ :	T					):





    """simple docstring"""



    UpperCamelCase__ 						=						value
    UpperCamelCase__ 						=						None
    UpperCamelCase__ 						=						None

 @property
 def 		lowercase_						( self :	Dict					):





    """simple docstring"""



    return self._value

 @staticmethod
 def 		lowercase_						( a_ :	RandomizedHeapNode[T] | None,					a_ :	RandomizedHeapNode[T] | None					):





    """simple docstring"""



    if not roota:
       return roota

    if not roota:
       return roota

    if roota.value > roota.value:
       UpperCamelCase__						,		UpperCamelCase__ 						=						roota, roota

    if random.choice([True, False]					):
       UpperCamelCase__						,		UpperCamelCase__ 						=						roota.right, roota.left

    UpperCamelCase__ 						=						RandomizedHeapNode.merge(roota.left,					a_					)

    return roota







class  UpperCAmelCase (       Generic[T]):

 def __init__( self :	List[Any],					a_ :	Iterable[T] | None = ()					):





    """simple docstring"""



    UpperCamelCase__ 						=						None

    if data:
       for item in data:
          self.insert(a_					)

 def 		lowercase_						( self :	Any,					a_ :	T					):





    """simple docstring"""



    UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root,					RandomizedHeapNode(a_					)					)

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.top()

    if self._root is None:
       return None

    UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root.left,					self._root.right					)

    return result

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    if not self._root:
       raise IndexError("Can't get top element for the empty heap."					)
    return self._root.value

 def 		lowercase_						( self :	Tuple					):





    """simple docstring"""



    UpperCamelCase__ 						=						None

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						[]
    while self:
       result.append(self.pop()					)

    return result

 def __bool__( self :	List[Any]					):





    """simple docstring"""



    return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()