



"""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       (  __A			:	str       ,		__A			:	Any       ,		__A			:	Union[str, Any]       ,		__A			:	Dict       ,		__A			:	Any       ,		__A			:	Tuple    )    ->					str:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    states.reshape(1       ,		-1       ,		model.config.state_dim    )
				SCREAMING_SNAKE_CASE	:       List[Any] 							=    actions.reshape(1       ,		-1       ,		model.config.act_dim    )
				SCREAMING_SNAKE_CASE	:       List[Any] 							=    returns_to_go.reshape(1       ,		-1       ,		1    )
				SCREAMING_SNAKE_CASE	:       Dict 							=    timesteps.reshape(1       ,		-1    )

				if model.config.max_length is not None:
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    states[:, -model.config.max_length :]
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    actions[:, -model.config.max_length :]
								SCREAMING_SNAKE_CASE	:       Dict 							=    returns_to_go[:, -model.config.max_length :]
								SCREAMING_SNAKE_CASE	:       List[str] 							=    timesteps[:, -model.config.max_length :]

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

				SCREAMING_SNAKE_CASE	:       List[str] 							=    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

A_	:						Dict				=			gym.make('Hopper-v3')
A_	:						Any				=			env.observation_space.shape[0]
A_	:						Optional[Any]				=			env.action_space.shape[0]
A_	:						Any				=			1000
A_	:						Tuple				=			'''cuda'''
A_	:						Optional[int]				=			1000.0  # normalization for rewards/returns
A_	:						Optional[Any]				=			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
A_	:						str				=			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,
    ]
)
A_	:						Dict				=			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,
    ]
)
A_	:						int				=			torch.from_numpy(state_mean).to(device=device)
A_	:						Tuple				=			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
A_	:						List[Any]				=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
A_	:						Any				=			model.to(device)
model.eval()

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

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

														A_	:						int				=			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_	:						List[str]				=			action
														A_	:						List[str]				=			action.detach().cpu().numpy()

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

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

														A_	:						str				=			target_return[0, -1] - (reward / scale)
														A_	:						Dict				=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
														A_	:						List[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


A_	:						Any				=			logging.get_logger(__name__)
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					*_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  None:





					"""simple docstring"""


					warnings.warn(
					    'The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use ConvNextImageProcessor instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
					super().__init__(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





