




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

			__UpperCamelCase				:      List[Any]									=       states.reshape(1 ,				-1 ,				model.config.state_dim						)
			__UpperCamelCase				:      Tuple									=       actions.reshape(1 ,				-1 ,				model.config.act_dim						)
			__UpperCamelCase				:      List[str]									=       returns_to_go.reshape(1 ,				-1 ,				1						)
			__UpperCamelCase				:      str									=       timesteps.reshape(1 ,				-1						)

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

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

			__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      int									=       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

_lowerCAmelCase				     = gym.make('''Hopper-v3''')
_lowerCAmelCase				     = env.observation_space.shape[0]
_lowerCAmelCase				     = env.action_space.shape[0]
_lowerCAmelCase				     = 1000
_lowerCAmelCase				     = '''cuda'''
_lowerCAmelCase				     = 1_000.0  # normalization for rewards/returns
_lowerCAmelCase				     = 3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase				     = 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,
    ]
)
_lowerCAmelCase				     = 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,
    ]
)
_lowerCAmelCase				     = torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase				     = torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
				_lowerCAmelCase     , _lowerCAmelCase				     = 0, 0
				_lowerCAmelCase				     = env.reset()
				_lowerCAmelCase				     = torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
				_lowerCAmelCase				     = torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
				_lowerCAmelCase				     = torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
				_lowerCAmelCase				     = torch.zeros(0, device=device, dtype=torch.floataa)

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

								_lowerCAmelCase				     = 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),
								)
								_lowerCAmelCase				     = action
								_lowerCAmelCase				     = action.detach().cpu().numpy()

								_lowerCAmelCase     , _lowerCAmelCase     , _lowerCAmelCase     , _lowerCAmelCase				     = env.step(action)

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

								_lowerCAmelCase				     = target_return[0, -1] - (reward / scale)
								_lowerCAmelCase				     = torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
								_lowerCAmelCase				     = 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

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

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

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





				def __bool__(self      )     ->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()



