
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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:str 		=    states.reshape(1     ,						-1     ,						model.config.state_dim							)
			UpperCAmelCase__		:Optional[int] 		=    actions.reshape(1     ,						-1     ,						model.config.act_dim							)
			UpperCAmelCase__		:Optional[Any] 		=    returns_to_go.reshape(1     ,						-1     ,						1							)
			UpperCAmelCase__		:Tuple 		=    timesteps.reshape(1     ,						-1							)

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

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

			UpperCAmelCase__		: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

__snake_case					:							Any           =							gym.make('Hopper-v3')
__snake_case					:							Optional[int]           =							env.observation_space.shape[0]
__snake_case					:							List[str]           =							env.action_space.shape[0]
__snake_case					:							int           =							1_000
__snake_case					:							List[str]           =							'''cuda'''
__snake_case					:							Optional[Any]           =							1_000.0  # normalization for rewards/returns
__snake_case					:							List[Any]           =							3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case					:							int           =							np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
__snake_case					:							Dict           =							np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
__snake_case					:							Dict           =							torch.from_numpy(state_mean).to(device=device)
__snake_case					:							List[Any]           =							torch.from_numpy(state_std).to(device=device)

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

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

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

												__snake_case					:							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),
												)
												__snake_case					:							Dict           =							action
												__snake_case					:							Optional[Any]           =							action.detach().cpu().numpy()

												__snake_case					:							Dict           =							env.step(action)

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

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











'''simple docstring'''





from __future__ import annotations

import sys





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    # base cases
    if abs(UpperCAmelCase_						) == 1:
        return a, 0
    elif abs(UpperCAmelCase_						) == 1:
        return 0, b

    _a       , _a				:							List[Any]							       =      a, b
    _a       , _a				:							str							       =      1, 0
    _a       , _a				:							Union[str, Any]							       =      0, 1

    while remainder != 0:
        _a				:							List[Any]							       =      old_remainder // remainder
        _a       , _a				:							str							       =      remainder, old_remainder - quotient * remainder
        _a       , _a				:							Optional[Any]							       =      coeff_a, old_coeff_a - quotient * coeff_a
        _a       , _a				:							Tuple							       =      coeff_b, old_coeff_b - quotient * coeff_b

    # sign correction for negative numbers
    if a < 0:
        _a				:							Tuple							       =      -old_coeff_a
    if b < 0:
        _a				:							Any							       =      -old_coeff_b

    return old_coeff_a, old_coeff_b





def UpperCAmelCase    (       ):
    if len(sys.argv						) < 3:
        print("""2 integer arguments required"""						)
        return 1
    _a				:							Union[str, Any]							       =      int(sys.argv[1]						)
    _a				:							Any							       =      int(sys.argv[2]						)
    print(extended_euclidean_algorithm(UpperCAmelCase_   ,	UpperCAmelCase_						)						)
    return 0


if __name__ == "__main__":
  raise SystemExit(main())


