




'''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_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
    # we don't care about the past rewards in this model

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

    if model.config.max_length is not None:
        _a				:							Dict							       =      states[:, -model.config.max_length :]
        _a				:							int							       =      actions[:, -model.config.max_length :]
        _a				:							int							       =      returns_to_go[:, -model.config.max_length :]
        _a				:							Union[str, Any]							       =      timesteps[:, -model.config.max_length :]

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

    _a       , _a       , _a				:							str							       =      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

a		:	Any       =      gym.make('''Hopper-v3''')
a		:	Optional[int]       =      env.observation_space.shape[0]
a		:	List[str]       =      env.action_space.shape[0]
a		:	int       =      1_0_0_0
a		:	List[str]       =      '''cuda'''
a		:	Optional[Any]       =      10_00.0  # normalization for rewards/returns
a		:	List[Any]       =      3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a		:	int       =      np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
a		:	Dict       =      np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
a		:	Dict       =      torch.from_numpy(state_mean).to(device=device)
a		:	List[Any]       =      torch.from_numpy(state_std).to(device=device)

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

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

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

    a		:	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),
    )
    a		:	Dict       =      action
    a		:	Optional[Any]       =      action.detach().cpu().numpy()

    a      ,				a      ,				a      ,				a		:	Dict       =      env.step(action)

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

    a		:	Optional[Any]       =      target_return[0, -1] - (reward / scale)
    a		:	Optional[int]       =      torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    a		:	Optional[int]       =      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 functools
import operator

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


a		:	Dict       =      logging.get_logger(__name__)

a		:	Optional[int]       =      {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}




class      UpperCamelCase       (	snake_case__       ):
  a     :int          =					'hubert'



  def __init__(			self		,     __lowerCAmelCase=32		,     __lowerCAmelCase=768		,     __lowerCAmelCase=12		,     __lowerCAmelCase=12		,     __lowerCAmelCase=3072		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.02		,     __lowerCAmelCase=1e-5		,     __lowerCAmelCase="group"		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)		,     __lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     __lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,     __lowerCAmelCase=False		,     __lowerCAmelCase=128		,     __lowerCAmelCase=16		,     __lowerCAmelCase=False		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.05		,     __lowerCAmelCase=10		,     __lowerCAmelCase=2		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=10		,     __lowerCAmelCase=0		,     __lowerCAmelCase="sum"		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=256		,     __lowerCAmelCase=0		,     __lowerCAmelCase=1		,     __lowerCAmelCase=2		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(**__lowerCAmelCase		,     pad_token_id=__lowerCAmelCase		,     bos_token_id=__lowerCAmelCase		,     eos_token_id=__lowerCAmelCase )
      _a				:							List[Any]							       =      hidden_size
      _a				:							Union[str, Any]							       =      feat_extract_norm
      _a				:							Any							       =      feat_extract_activation
      _a				:							Tuple							       =      list(__lowerCAmelCase )
      _a				:							List[Any]							       =      list(__lowerCAmelCase )
      _a				:							Any							       =      list(__lowerCAmelCase )
      _a				:							Optional[int]							       =      conv_bias
      _a				:							Tuple							       =      num_conv_pos_embeddings
      _a				:							Optional[int]							       =      num_conv_pos_embedding_groups
      _a				:							Tuple							       =      len(self.conv_dim )
      _a				:							int							       =      num_hidden_layers
      _a				:							Optional[Any]							       =      intermediate_size
      _a				:							Dict							       =      hidden_act
      _a				:							Tuple							       =      num_attention_heads
      _a				:							Tuple							       =      hidden_dropout
      _a				:							str							       =      attention_dropout
      _a				:							Union[str, Any]							       =      activation_dropout
      _a				:							int							       =      feat_proj_layer_norm
      _a				:							List[Any]							       =      feat_proj_dropout
      _a				:							Any							       =      final_dropout
      _a				:							Dict							       =      layerdrop
      _a				:							List[str]							       =      layer_norm_eps
      _a				:							Any							       =      initializer_range
      _a				:							List[str]							       =      vocab_size
      _a				:							int							       =      do_stable_layer_norm
      _a				:							str							       =      use_weighted_layer_sum
      _a				:							Optional[Any]							       =      classifier_proj_size

      if (
          (len(self.conv_stride ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel ) != self.num_feat_extract_layers)
          or (len(self.conv_dim ) != self.num_feat_extract_layers)
      ):
          raise ValueError(
              """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
              """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
              f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
              f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _a				:							str							       =      apply_spec_augment
      _a				:							List[Any]							       =      mask_time_prob
      _a				:							List[Any]							       =      mask_time_length
      _a				:							List[Any]							       =      mask_time_min_masks
      _a				:							Union[str, Any]							       =      mask_feature_prob
      _a				:							str							       =      mask_feature_length
      _a				:							Any							       =      mask_feature_min_masks

      # ctc loss
      _a				:							Tuple							       =      ctc_loss_reduction
      _a				:							Optional[int]							       =      ctc_zero_infinity



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return functools.reduce(operator.mul		,     self.conv_stride		,     1 )


