


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(				__a							:	List[Any]						,    __a							:	Optional[int]						,    __a							:	Optional[int]						,    __a							:	Tuple						,    __a							:	int						,    __a							:	int    ):
							# we don't care about the past rewards in this model

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

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

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

							a__						,    a__						,    a__     						=model(
							    states=__a						,    actions=__a						,    rewards=__a						,    returns_to_go=__a						,    timesteps=__a						,    attention_mask=__a						,    return_dict=__a						,    )

							return action_preds[0, -1]


# build the environment

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

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

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

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

		_lowerCAmelCase:	List[str]      							=					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:	Optional[int]      							=					action
		_lowerCAmelCase:	Optional[int]      							=					action.detach().cpu().numpy()

		_lowerCAmelCase							,	_lowerCAmelCase							,	_lowerCAmelCase							,	_lowerCAmelCase:	Any      							=					env.step(action)

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

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






def      _lowercase(				__a							:	float | int						,    __a							:	float | int						,    __a							:	float | int						,    ):
							if not all((nth_term, start_term_a, common_ratio_r)    ):
														return []
							a__     						=[]
							a__     						=1
							a__     						=common_ratio_r
							for _ in range(int(__a    )    ):
														if not series:
																					series.append(__a    )
														else:
																					power += 1
																					series.append(float(start_term_a * multiple    )    )
																					a__     						=pow(float(__a    )						,    __a    )
							return series


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCAmelCase:	Tuple      							=					float(input('Enter the last number (n term) of the Geometric Series'))
	_lowerCAmelCase:	Optional[int]      							=					float(input('Enter the starting term (a) of the Geometric Series'))
	_lowerCAmelCase:	List[Any]      							=					float(
	    input('Enter the common ratio between two terms (r) of the Geometric Series')
	)
	print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
	print(geometric_series(nth_term, start_term_a, common_ratio_r))

