
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       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     str:
			UpperCamelCase__    :		Optional[Any]     =				states.reshape(1				,    -1				,    model.config.state_dim )
			UpperCamelCase__    :		List[Any]     =				actions.reshape(1				,    -1				,    model.config.act_dim )
			UpperCamelCase__    :		List[Any]     =				returns_to_go.reshape(1				,    -1				,    1 )
			UpperCamelCase__    :		Dict     =				timesteps.reshape(1				,    -1 )

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

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

			UpperCamelCase__    :		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

UpperCAmelCase__				:  Dict       					=				gym.make('''Hopper-v3''')
UpperCAmelCase__				:  Any       					=				env.observation_space.shape[0]
UpperCAmelCase__				:  Optional[Any]       					=				env.action_space.shape[0]
UpperCAmelCase__				:  Any       					=				1_000
UpperCAmelCase__				:  Tuple       					=				'''cuda'''
UpperCAmelCase__				:  Optional[int]       					=				1_000.0  # normalization for rewards/returns
UpperCAmelCase__				:  Optional[Any]       					=				3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCAmelCase__				:  str       					=				np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
UpperCAmelCase__				:  Dict       					=				np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
UpperCAmelCase__				:  int       					=				torch.from_numpy(state_mean).to(device=device)
UpperCAmelCase__				:  Tuple       					=				torch.from_numpy(state_std).to(device=device)

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

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

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

														UpperCAmelCase__				:  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),
														)
														UpperCAmelCase__				:  List[str]       					=				action
														UpperCAmelCase__				:  List[str]       					=				action.detach().cpu().numpy()

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

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

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





from __future__ import annotations

import re



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     list[str]:

			def alphanum_key(__SCREAMING_SNAKE_CASE ):
						return [int(__SCREAMING_SNAKE_CASE ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'				,    __SCREAMING_SNAKE_CASE )]

			return sorted(__SCREAMING_SNAKE_CASE				,    key=__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



