


'''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      __snake_case       (       lowerCamelCase_ :				Optional[Any]	,  lowerCamelCase_ :				str	,  lowerCamelCase_ :				int	,  lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




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

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

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

				__magic_name__  ,      __magic_name__  ,      __magic_name__	  = 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

__magic_name__   :   str  							=gym.make('Hopper-v3')
__magic_name__   :   List[str]  							=env.observation_space.shape[0]
__magic_name__   :   Tuple  							=env.action_space.shape[0]
__magic_name__   :   Optional[int]  							=10_00
__magic_name__   :   Optional[Any]  							='cuda'
__magic_name__   :   Tuple  							=1_0_0_0.0  # normalization for rewards/returns
__magic_name__   :   List[str]  							=36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__magic_name__   :   str  							=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,
    ]
)
__magic_name__   :   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,
    ]
)
__magic_name__   :   int  							=torch.from_numpy(state_mean).to(device=device)
__magic_name__   :   List[Any]  							=torch.from_numpy(state_std).to(device=device)

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

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

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

												__magic_name__   :   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),
												)
												__magic_name__   :   Any  							=action
												__magic_name__   :   int  							=action.detach().cpu().numpy()

												__magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__   :   Any  							=env.step(action)

												__magic_name__   :   Optional[int]  							=torch.from_numpy(state).to(device=device).reshape(1, state_dim)
												__magic_name__   :   Optional[int]  							=torch.cat([states, cur_state], dim=0)
												__magic_name__   :   Dict  							=reward

												__magic_name__   :   Optional[Any]  							=target_return[0, -1] - (reward / scale)
												__magic_name__   :   int  							=torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												__magic_name__   :   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 warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__magic_name__   :   int  							=logging.get_logger(__name__)






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						def __init__( self				:					List[str]	,				*_lowerCamelCase				:					List[str]	,				**_lowerCamelCase				:					Optional[Any]  )							->					None:
										warnings.warn(
										    "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use ConvNextImageProcessor instead."	,				_lowerCamelCase	,				)
										super().__init__(*_lowerCamelCase	,				**_lowerCamelCase  )



