



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 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Any:







						"""simple docstring"""

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

						if model.config.max_length is not None:
												A__						:				Any         =			states[:, -model.config.max_length :]
												A__						:				int         =			actions[:, -model.config.max_length :]
												A__						:				Any         =			returns_to_go[:, -model.config.max_length :]
												A__						:				Tuple         =			timesteps[:, -model.config.max_length :]

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

						A__						:				Union[str, Any]         =			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

snake_case     :    Dict						=    gym.make('Hopper-v3')
snake_case     :    Union[str, Any]						=    env.observation_space.shape[0]
snake_case     :    Optional[int]						=    env.action_space.shape[0]
snake_case     :    List[Any]						=    1_0_0_0
snake_case     :    Any						=    'cuda'
snake_case     :    str						=    1_0_0_0.0  # normalization for rewards/returns
snake_case     :    int						=    3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case     :    int						=    np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
snake_case     :    List[Any]						=    np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
snake_case     :    str						=    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     :    Any						=    DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
snake_case     :    int						=    model.to(device)
model.eval()

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

		snake_case     :    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     :    str						=    torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
				snake_case     :    List[Any]						=    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     :    Tuple						=    action
				snake_case     :    str						=    action.detach().cpu().numpy()

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

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

				snake_case     :    Optional[int]						=    target_return[0, -1] - (reward / scale)
				snake_case     :    Tuple						=    torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
				snake_case     :    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
'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



