



'''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 	lowercase_						( lowerCAmelCase__						:	Any      ,				lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	List[str]      ,				lowerCAmelCase__						:	Optional[Any]      ,				lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	Any			):

			"""simple docstring"""





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

			if model.config.max_length is not None:
						__UpperCAmelCase :   Tuple    					=  states[:, -model.config.max_length :]
						__UpperCAmelCase :   Optional[int]    					=  actions[:, -model.config.max_length :]
						__UpperCAmelCase :   Optional[Any]    					=  returns_to_go[:, -model.config.max_length :]
						__UpperCAmelCase :   Dict    					=  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 :   Any    					=  attention_mask.to(dtype=torch.long      ,				device=states.device			).reshape(1      ,				-1			)
						__UpperCAmelCase :   Optional[int]    					=  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 :   Any    					=  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 :   Union[str, Any]    					=  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 :   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:
						__UpperCAmelCase :   int    					=  None

			__UpperCAmelCase					,	__UpperCAmelCase					,	__UpperCAmelCase :   Union[str, Any]    					=  model(
			    states=lowerCAmelCase__      ,				actions=lowerCAmelCase__      ,				rewards=lowerCAmelCase__      ,				returns_to_go=lowerCAmelCase__      ,				timesteps=lowerCAmelCase__      ,				attention_mask=lowerCAmelCase__      ,				return_dict=lowerCAmelCase__      ,				)

			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									=      1_000.0  # normalization for rewards/returns
_UpperCamelCase									=      3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase									=      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,
    ]
)
_UpperCamelCase									=      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,
    ]
)
_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









'''simple docstring'''
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment



class _A	(     unittest.TestCase     ):





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  inspect.getfile(accelerate.test_utils					)
					__UpperCAmelCase :   int    					=  os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["""scripts""", """test_sync.py"""]					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main			,							num_processes=1					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main					)





		@require_single_gpu
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					test_sync.main()





		@require_multi_gpu
		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					print(f'Found {torch.cuda.device_count()} devices.'					)
					__UpperCAmelCase :   List[str]    					=  ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
					with patch_environment(omp_num_threads=1					):
								execute_subprocess_async(__UpperCAmelCase			,							env=os.environ.copy()					)




