


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__				(    a__:	Union[str, Any]    ,						a__:	Any    ,						a__:	Optional[int]    ,						a__:	Any    ,						a__:	Any    ,						a__:	int )						->	Union[str, Any]:


							'''simple docstring'''





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

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

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

							_UpperCAmelCase	, _UpperCAmelCase	, _UpperCAmelCase					=    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

lowerCAmelCase__						:List[str]         =							gym.make('''Hopper-v3''')
lowerCAmelCase__						:str         =							env.observation_space.shape[0]
lowerCAmelCase__						:Optional[int]         =							env.action_space.shape[0]
lowerCAmelCase__						:Union[str, Any]         =							1_0_0_0
lowerCAmelCase__						:Dict         =							'''cuda'''
lowerCAmelCase__						:List[Any]         =							1_000.0  # normalization for rewards/returns
lowerCAmelCase__						:List[str]         =							3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowerCAmelCase__						:Tuple         =							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__						:Optional[int]         =							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__						:List[Any]         =							torch.from_numpy(state_mean).to(device=device)
lowerCAmelCase__						:Optional[Any]         =							torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
					lowerCAmelCase__    ,						lowerCAmelCase__						:str         =							0, 0
					lowerCAmelCase__						:Union[str, Any]         =							env.reset()
					lowerCAmelCase__						:Optional[Any]         =							torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
					lowerCAmelCase__						:Any         =							torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
					lowerCAmelCase__						:int         =							torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
					lowerCAmelCase__						:Optional[Any]         =							torch.zeros(0, device=device, dtype=torch.floataa)

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

										lowerCAmelCase__						: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),
										)
										lowerCAmelCase__						:Tuple         =							action
										lowerCAmelCase__						:Any         =							action.detach().cpu().numpy()

										lowerCAmelCase__    ,						lowerCAmelCase__    ,						lowerCAmelCase__    ,						lowerCAmelCase__						:int         =							env.step(action)

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

										lowerCAmelCase__						:Optional[int]         =							target_return[0, -1] - (reward / scale)
										lowerCAmelCase__						:List[str]         =							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
										lowerCAmelCase__						:Tuple         =							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




import warnings

from .generation import GenerationMixin

class 	__a   (       UpperCAmelCase    ):
		# warning at import time
		warnings.warn(
		    'Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'					,   UpperCAmelCase					,   )
