





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__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Tuple							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Any    )      ->			Dict:



					'''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						=					model(
					    states=UpperCamelCase__							,     actions=UpperCamelCase__							,     rewards=UpperCamelCase__							,     returns_to_go=UpperCamelCase__							,     timesteps=UpperCamelCase__							,     attention_mask=UpperCamelCase__							,     return_dict=UpperCamelCase__							,     )

					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_   				=  1000.0  # normalization for rewards/returns
UpperCAmelCase_   				=  3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCAmelCase_   				=  np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
UpperCAmelCase_   				=  np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
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    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Dict				      =       inspect.getfile(accelerate.test_utils   )
										snake_case__							:       Tuple				      =       os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_sync.py''']   )

				@require_cpu
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										debug_launcher(test_sync.main   ,num_processes=1   )

				@require_cpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										debug_launcher(test_sync.main   )

				@require_single_gpu
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										test_sync.main()





				@require_multi_gpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										print(F'''Found {torch.cuda.device_count()} devices.'''   )
										snake_case__							:       Dict				      =       ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
										with patch_environment(omp_num_threads=1   ):
																execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()   )


