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

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

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

  __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
    __snake_case  : List[Any]		       =    torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    __snake_case  : 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  : int		       =    action.detach().cpu().numpy()

    __snake_case       ,       __snake_case       ,       __snake_case       ,       __snake_case  : str		       =    env.step(action)

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

    __snake_case  : Tuple		       =    target_return[0, -1] - (reward / scale)
    __snake_case  : List[str]		       =    torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    __snake_case  : Any		       =    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 json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case  : Dict		       =    500_000

__snake_case       ,       __snake_case  : List[str]		       =    os.path.split(__file__)
__snake_case  : Optional[Any]		       =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> Union[str, Any]:
    A_      =  dataset.select(range(0,  len(_UpperCamelCase  ),  2  )  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> str:
    A_      =  dataset.sort('''numbers'''  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> str:
    A_      =  dataset.shuffle()





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> Optional[Any]:
    A_      =  dataset.train_test_split(0.1  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset,  _UpperCamelCase       :      Optional[int]=10  )      -> Dict:
    for shard_id in range(_UpperCamelCase  ):
        A_      =  dataset.shard(_UpperCamelCase,  _UpperCamelCase  )





def 	_UpperCAmelCase       (				)      -> Any:
    A_      =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
    A_      =  (select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''  )
        A_      =  datasets.Features({'''text''': datasets.Value('''string'''  ), '''numbers''': datasets.Value('''float32'''  )}  )
        A_      =  generate_example_dataset(
            os.path.join(_UpperCamelCase,  '''dataset.arrow'''  ),  _UpperCamelCase,  num_examples=_UpperCamelCase  )
        print('''Functions'''  )
        for func in functions:
            print(func.__name__  )
            A_      =  func(_UpperCamelCase  )

    with open(_UpperCamelCase,  '''wb'''  ) as f:
        f.write(json.dumps(_UpperCamelCase  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_indices_mapping()






