


'''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			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		# we don't care about the past rewards in this model

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

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

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

		_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   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

lowerCamelCase__     						=  gym.make('Hopper-v3')
lowerCamelCase__     						=  env.observation_space.shape[0]
lowerCamelCase__     						=  env.action_space.shape[0]
lowerCamelCase__     						=  1_000
lowerCamelCase__     						=  'cuda'
lowerCamelCase__     						=  1_000.0  # normalization for rewards/returns
lowerCamelCase__     						=  3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowerCamelCase__     						=  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,
    ]
)
lowerCamelCase__     						=  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,
    ]
)
lowerCamelCase__     						=  torch.from_numpy(state_mean).to(device=device)
lowerCamelCase__     						=  torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
						lowerCamelCase__						,lowerCamelCase__     						=  0, 0
						lowerCamelCase__     						=  env.reset()
						lowerCamelCase__     						=  torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						lowerCamelCase__     						=  torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
						lowerCamelCase__     						=  torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
						lowerCamelCase__     						=  torch.zeros(0, device=device, dtype=torch.floataa)

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

												lowerCamelCase__     						=  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),
												)
												lowerCamelCase__     						=  action
												lowerCamelCase__     						=  action.detach().cpu().numpy()

												lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__     						=  env.step(action)

												lowerCamelCase__     						=  torch.from_numpy(state).to(device=device).reshape(1, state_dim)
												lowerCamelCase__     						=  torch.cat([states, cur_state], dim=0)
												lowerCamelCase__     						=  reward

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


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__     						=  logging.get_logger(__name__)

lowerCamelCase__     						=  {
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}






class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__						):
			lowerCAmelCase			:						Optional[Any]							 =  "convnext"




			def __init__(		self	:  Optional[int]						,	lowerCamelCase__	:  List[str]=3						,	lowerCamelCase__	:  List[str]=4						,	lowerCamelCase__	:  Optional[int]=4						,	lowerCamelCase__	:  Dict=None						,	lowerCamelCase__	:  Tuple=None						,	lowerCamelCase__	:  List[str]="gelu"						,	lowerCamelCase__	:  List[Any]=0.0_2						,	lowerCamelCase__	:  Dict=1E-12						,	lowerCamelCase__	:  Tuple=1E-6						,	lowerCamelCase__	:  Dict=0.0						,	lowerCamelCase__	:  Optional[int]=2_24						,	lowerCamelCase__	:  List[Any]=None						,	lowerCamelCase__	:  Any=None						,	**lowerCamelCase__	:  int						,	) ->str:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__			)

					_UpperCAmelCase		:							Optional[int]             =   num_channels
					_UpperCAmelCase		:							Tuple             =   patch_size
					_UpperCAmelCase		:							Dict             =   num_stages
					_UpperCAmelCase		:							Optional[int]             =   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
					_UpperCAmelCase		:							Dict             =   [3, 3, 9, 3] if depths is None else depths
					_UpperCAmelCase		:							Union[str, Any]             =   hidden_act
					_UpperCAmelCase		:							Tuple             =   initializer_range
					_UpperCAmelCase		:							Tuple             =   layer_norm_eps
					_UpperCAmelCase		:							Tuple             =   layer_scale_init_value
					_UpperCAmelCase		:							str             =   drop_path_rate
					_UpperCAmelCase		:							str             =   image_size
					_UpperCAmelCase		:							Tuple             =   ["stem"] + [F"""stage{idx}""" for idx in range(1						,	len(self.depths			) + 1			)]
					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   get_aligned_output_features_output_indices(
					    out_features=lowerCamelCase__						,	out_indices=lowerCamelCase__						,	stage_names=self.stage_names			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):
			lowerCAmelCase			:						str							 =  version.parse("1.11"						)




			@property
			def 				lowerCAmelCase__     (		self	:  Dict			) ->Mapping[str, Mapping[int, str]]:



					'''simple docstring'''







					return OrderedDict(
					    [
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ]			)






			@property
			def 				lowerCAmelCase__     (		self	:  str			) ->float:



					'''simple docstring'''







					return 1E-5



