"""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   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       Union[str, Any],				_snake_case		:       str,				_snake_case		:       Any,				_snake_case		:       int,				_snake_case		:       Any       ):
   _lowercase							     =       states.reshape(1,				-1,				model.config.state_dim       )
   _lowercase							     =       actions.reshape(1,				-1,				model.config.act_dim       )
   _lowercase							     =       returns_to_go.reshape(1,				-1,				1       )
   _lowercase							     =       timesteps.reshape(1,				-1       )

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

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

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

# Create the decision transformer model
__UpperCamelCase       :    Tuple         =		DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
__UpperCamelCase       :    str         =		model.to(device)
model.eval()

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

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

    __UpperCamelCase       :    Optional[Any]         =		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       :    Optional[Any]         =		action
    __UpperCamelCase       :    int         =		action.detach().cpu().numpy()

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

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

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




def 				A      (	_UpperCAmelCase							:  List[Any]       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  len(_UpperCAmelCase       )
			for _ in range(
			    0				,  int(((no_of_elements - 1) / 2) + 1       )       ):  # we don't need to traverse to end of list as
						for j in range(0				,  no_of_elements - 1       ):
									if (
									    lst[j + 1] < lst[j]
									):  # applying bubble sort algorithm from left to right (or forwards)
												_UpperCAmelCase									=  lst[j + 1]
												_UpperCAmelCase									=  lst[j]
												_UpperCAmelCase									=  temp
									if (
									    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
									):  # applying bubble sort algorithm from right to left (or backwards)
												_UpperCAmelCase									=  lst[no_of_elements - 1 - j]
												_UpperCAmelCase									=  lst[no_of_elements - 2 - j]
												_UpperCAmelCase									=  temp
			return lst


if __name__ == "__main__":
							print("enter the list to be sorted")
							UpperCAmelCase__  =			[int(x) for x in input().split()]  # inputing elements of the list in one line
							UpperCAmelCase__  =			double_sort(lst)
							print("the sorted list is")
							print(sorted_lst)
