



'''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    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     Any:
							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	:						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

a  :     List[Any]       =       gym.make("""Hopper-v3""")
a  :     int       =       env.observation_space.shape[0]
a  :     Any       =       env.action_space.shape[0]
a  :     str       =       1_0_0_0
a  :     str       =       """cuda"""
a  :     List[Any]       =       1_0_0_0.0  # normalization for rewards/returns
a  :     List[str]       =       3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a  :     Dict       =       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,
    ]
)
a  :     Tuple       =       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,
    ]
)
a  :     int       =       torch.from_numpy(state_mean).to(device=device)
a  :     str       =       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
a  :     Optional[Any]       =       DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
a  :     Dict       =       model.to(device)
model.eval()

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

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

														a  :     Union[str, 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),
														)
														a  :     Union[str, Any]       =       action
														a  :     Any       =       action.detach().cpu().numpy()

														a    , a    , a    , a  :     List[str]       =       env.step(action)

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

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




