



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 :  Tuple		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  int		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						states.reshape(1		,						-1		,						model.config.state_dim  )
 __SCREAMING_SNAKE_CASE							:     int							=						actions.reshape(1		,						-1		,						model.config.act_dim  )
 __SCREAMING_SNAKE_CASE							:     int							=						returns_to_go.reshape(1		,						-1		,						1  )
 __SCREAMING_SNAKE_CASE							:     int							=						timesteps.reshape(1		,						-1  )

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

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

 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						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

_lowerCamelCase         =	gym.make("""Hopper-v3""")
_lowerCamelCase         =	env.observation_space.shape[0]
_lowerCamelCase         =	env.action_space.shape[0]
_lowerCamelCase         =	1000
_lowerCamelCase         =	"""cuda"""
_lowerCamelCase         =	1_0_0_0.0  # normalization for rewards/returns
_lowerCamelCase         =	3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCamelCase         =	np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
_lowerCamelCase         =	np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
_lowerCamelCase         =	torch.from_numpy(state_mean).to(device=device)
_lowerCamelCase         =	torch.from_numpy(state_std).to(device=device)

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

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

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

            _lowerCamelCase         =	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),
            )
            _lowerCamelCase         =	action
            _lowerCamelCase         =	action.detach().cpu().numpy()

            _lowerCamelCase     ,							_lowerCamelCase     ,							_lowerCamelCase     ,							_lowerCamelCase         =	env.step(action)

            _lowerCamelCase         =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
            _lowerCamelCase         =	torch.cat([states, cur_state], dim=0)
            _lowerCamelCase         =	reward

            _lowerCamelCase         =	target_return[0, -1] - (reward / scale)
            _lowerCamelCase         =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            _lowerCamelCase         =	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 torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
