



'''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 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Optional[int] ,       snake_case_    :     Tuple ,       snake_case_    :     Union[str, Any] ,       snake_case_    :     Tuple ,       snake_case_    :     List[str] ,       snake_case_    :     Optional[Any]  )		->List[str]:






							'''simple docstring'''
							# we don't care about the past rewards in this model

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

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

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

							_lowercase			, _lowercase			, _lowercase							:		int				=   model(
							    states=snake_case_ ,       actions=snake_case_ ,       rewards=snake_case_ ,       returns_to_go=snake_case_ ,       timesteps=snake_case_ ,       attention_mask=snake_case_ ,       return_dict=snake_case_ ,       )

							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__	  =		10_00
lowerCamelCase__	  =		'cuda'
lowerCamelCase__	  =		1000.0  # normalization for rewards/returns
lowerCamelCase__	  =		36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowerCamelCase__	  =		np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
lowerCamelCase__	  =		np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
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'''






import warnings

from .generation import FlaxGenerationMixin

class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		# warning at import time
		warnings.warn(
		    'Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'     ,		__A     ,		)



