






'''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__		(					a_  :     Union[str, Any]			,						a_  :     Optional[Any]			,						a_  :     List[Any]			,						a_  :     int			,						a_  :     str			,						a_  :     Optional[int]			)   -> Union[str, Any]:
       UpperCAmelCase__	:   Union[str, Any]												=	states.reshape(1			,						-1			,						model.config.state_dim			)
       UpperCAmelCase__	:   List[str]												=	actions.reshape(1			,						-1			,						model.config.act_dim			)
       UpperCAmelCase__	:   List[Any]												=	returns_to_go.reshape(1			,						-1			,						1			)
       UpperCAmelCase__	:   str												=	timesteps.reshape(1			,						-1			)

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

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

       UpperCAmelCase__	:   Optional[Any]												=	model(
           states=__snake_case			,						actions=__snake_case			,						rewards=__snake_case			,						returns_to_go=__snake_case			,						timesteps=__snake_case			,						attention_mask=__snake_case			,						return_dict=__snake_case			,						)

       return action_preds[0, -1]


# build the environment

UpperCamelCase_					=				gym.make("Hopper-v3")
UpperCamelCase_					=				env.observation_space.shape[0]
UpperCamelCase_					=				env.action_space.shape[0]
UpperCamelCase_					=				1000
UpperCamelCase_					=				"cuda"
UpperCamelCase_					=				1000.0  # normalization for rewards/returns
UpperCamelCase_					=				3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase_					=				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,
    ]
)
UpperCamelCase_					=				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,
    ]
)
UpperCamelCase_					=				torch.from_numpy(state_mean).to(device=device)
UpperCamelCase_					=				torch.from_numpy(state_std).to(device=device)

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

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

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

    UpperCamelCase_					=				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_					=				action
    UpperCamelCase_					=				action.detach().cpu().numpy()

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

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

    UpperCamelCase_					=				target_return[0, -1] - (reward / scale)
    UpperCamelCase_					=				torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    UpperCamelCase_					=				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




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase								=      logging.get_logger(__name__)

__lowerCamelCase								=      {
    '''MCG-NJU/videomae-base''': '''https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json''',
}


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       _lowerCamelCase           =					"""videomae"""



       def __init__(  self  ,lowercase=224  ,lowercase=16  ,lowercase=3  ,lowercase=16  ,lowercase=2  ,lowercase=768  ,lowercase=12  ,lowercase=12  ,lowercase=3072  ,lowercase="gelu"  ,lowercase=0.0  ,lowercase=0.0  ,lowercase=0.02  ,lowercase=1E-12  ,lowercase=True  ,lowercase=True  ,lowercase=6  ,lowercase=384  ,lowercase=4  ,lowercase=1536  ,lowercase=True  ,**lowercase  ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_	:						Optional[int]					   =		image_size
             UpperCAmelCase_	:						Optional[Any]					   =		patch_size
             UpperCAmelCase_	:						Tuple					   =		num_channels
             UpperCAmelCase_	:						Optional[int]					   =		num_frames
             UpperCAmelCase_	:						str					   =		tubelet_size

             UpperCAmelCase_	:						Any					   =		hidden_size
             UpperCAmelCase_	:						List[str]					   =		num_hidden_layers
             UpperCAmelCase_	:						List[str]					   =		num_attention_heads
             UpperCAmelCase_	:						Union[str, Any]					   =		intermediate_size
             UpperCAmelCase_	:						List[str]					   =		hidden_act
             UpperCAmelCase_	:						Tuple					   =		hidden_dropout_prob
             UpperCAmelCase_	:						Optional[Any]					   =		attention_probs_dropout_prob
             UpperCAmelCase_	:						Optional[Any]					   =		initializer_range
             UpperCAmelCase_	:						Dict					   =		layer_norm_eps
             UpperCAmelCase_	:						Tuple					   =		qkv_bias
             UpperCAmelCase_	:						int					   =		use_mean_pooling

             UpperCAmelCase_	:						List[Any]					   =		decoder_num_attention_heads
             UpperCAmelCase_	:						Optional[int]					   =		decoder_hidden_size
             UpperCAmelCase_	:						Any					   =		decoder_num_hidden_layers
             UpperCAmelCase_	:						Dict					   =		decoder_intermediate_size
             UpperCAmelCase_	:						str					   =		norm_pix_loss
