






"""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	:		List[Any]		,    _lowerCamelCase	:		str		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Dict		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		List[Any]			)				->    Union[str, Any]:
  # we don't care about the past rewards in this model

  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

_SCREAMING_SNAKE_CASE :    List[Any]    =						gym.make('''Hopper-v3''')
_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						env.observation_space.shape[0]
_SCREAMING_SNAKE_CASE :    Any    =						env.action_space.shape[0]
_SCREAMING_SNAKE_CASE :    Optional[Any]    =						1000
_SCREAMING_SNAKE_CASE :    str    =						'''cuda'''
_SCREAMING_SNAKE_CASE :    str    =						1000.0  # normalization for rewards/returns
_SCREAMING_SNAKE_CASE :    Tuple    =						3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_SCREAMING_SNAKE_CASE :    List[Any]    =						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,
    ]
)
_SCREAMING_SNAKE_CASE :    List[str]    =						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,
    ]
)
_SCREAMING_SNAKE_CASE :    Optional[Any]    =						torch.from_numpy(state_mean).to(device=device)
_SCREAMING_SNAKE_CASE :    Dict    =						torch.from_numpy(state_std).to(device=device)

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

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

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

    _SCREAMING_SNAKE_CASE :    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),
    )
    _SCREAMING_SNAKE_CASE :    Tuple    =						action
    _SCREAMING_SNAKE_CASE :    Tuple    =						action.detach().cpu().numpy()

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

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

    _SCREAMING_SNAKE_CASE :    List[Any]    =						target_return[0, -1] - (reward / scale)
    _SCREAMING_SNAKE_CASE :    List[Any]    =						torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    _SCREAMING_SNAKE_CASE :    List[str]    =						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 random import randrange







def 					lowerCamelCase__	(							_lowerCamelCase	:		list			)				->    list:
  if len(_lowerCamelCase			) < 2:
    return collection
  lowerCamelCase_   					=						randrange(len(_lowerCamelCase			)			)  # Use random element as pivot
  lowerCamelCase_   					=						collection[pivot_index]
  lowerCamelCase_   					=						[]  # All elements greater than pivot
  lowerCamelCase_   					=						[]  # All elements less than or equal to pivot

  for element in collection[:pivot_index]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  for element in collection[pivot_index + 1 :]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  return [*quick_sort(_lowerCamelCase			), pivot, *quick_sort(_lowerCamelCase			)]


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Dict    =						input('''Enter numbers separated by a comma:\n''').strip()
  _SCREAMING_SNAKE_CASE :    List[str]    =						[int(item) for item in user_input.split(''',''')]
  print(quick_sort(unsorted))




