"""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    )							-> Any:




						'''simple docstring'''



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

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

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

						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ =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

a							=gym.make('Hopper-v3')
a							=env.observation_space.shape[0]
a							=env.action_space.shape[0]
a							=1000
a							='cuda'
a							=1_000.0  # normalization for rewards/returns
a							=3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a							=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,
    ]
)
a							=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,
    ]
)
a							=torch.from_numpy(state_mean).to(device=device)
a							=torch.from_numpy(state_std).to(device=device)

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

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

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

		a							=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							=action
		a							=action.detach().cpu().numpy()

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

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

		a							=target_return[0, -1] - (reward / scale)
		a							=torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		a							=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 __future__ import annotations

from collections.abc import Generator





def       lowerCamelCase_			( __lowerCAmelCase    )							-> Generator[int, None, None]:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ) or n < 1:
												raise Exception("Sequence only defined for positive integers"    )

						yield n
						while n != 1:
												if n % 2 == 0:
																		n //= 2
												else:
																		lowerCamelCase__ =3 * n + 1
												yield n





def       lowerCamelCase_			( )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =43
						lowerCamelCase__ =tuple(collatz_sequence(__lowerCAmelCase    )    )
						print(__lowerCAmelCase    )
						print(F'''Collatz sequence from {n} took {len(__lowerCAmelCase    )} steps.'''    )


if __name__ == "__main__":
	main()


