code: 
  
  
  '''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							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  					# we don't care about the past rewards in this model
  
  					__a    : int      					=						states.reshape(1		,   -1		,   model.config.state_dim     )
  					__a    : List[str]      					=						actions.reshape(1		,   -1		,   model.config.act_dim     )
  					__a    : List[str]      					=						returns_to_go.reshape(1		,   -1		,   1     )
  					__a    : Optional[Any]      					=						timesteps.reshape(1		,   -1     )
  
  					if model.config.max_length is not None:
  										__a    : Union[str, Any]      					=						states[:, -model.config.max_length :]
  										__a    : List[Any]      					=						actions[:, -model.config.max_length :]
  										__a    : List[Any]      					=						returns_to_go[:, -model.config.max_length :]
  										__a    : Optional[int]      					=						timesteps[:, -model.config.max_length :]
  
  										# pad all tokens to sequence length
  										__a    : Any      					=						torch.cat(
  										    [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
  										__a    : Union[str, Any]      					=						attention_mask.to(dtype=torch.long		,   device=states.device     ).reshape(1		,   -1     )
  										__a    : Dict      					=						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    : 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    : 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    : Optional[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:
  										__a    : Dict      					=						None
  
  					__a					,       __a					,       __a    : Union[str, Any]      					=						model(
  					    states=_SCREAMING_SNAKE_CASE		,   actions=_SCREAMING_SNAKE_CASE		,   rewards=_SCREAMING_SNAKE_CASE		,   returns_to_go=_SCREAMING_SNAKE_CASE		,   timesteps=_SCREAMING_SNAKE_CASE		,   attention_mask=_SCREAMING_SNAKE_CASE		,   return_dict=_SCREAMING_SNAKE_CASE		,   )
  
  					return action_preds[0, -1]
  
  
  # build the environment
  
  __lowercase			:		Any =			gym.make('Hopper-v3')
  __lowercase			:		Union[str, Any] =			env.observation_space.shape[0]
  __lowercase			:		List[Any] =			env.action_space.shape[0]
  __lowercase			:		Dict =			10_00
  __lowercase			:		Tuple =			'cuda'
  __lowercase			:		int =			10_00.0  # normalization for rewards/returns
  __lowercase			:		Optional[Any] =			36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __lowercase			:		List[str] =			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,
      ]
  )
  __lowercase			:		Tuple =			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,
      ]
  )
  __lowercase			:		Union[str, Any] =			torch.from_numpy(state_mean).to(device=device)
  __lowercase			:		int =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __lowercase			:		List[Any] =			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
  __lowercase			:		Any =			model.to(device)
  model.eval()
  
  for ep in range(10):
  	__lowercase ,   __lowercase			:		Any =			0, 0
  	__lowercase			:		int =			env.reset()
  	__lowercase			:		List[str] =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  	__lowercase			:		List[Any] =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  	__lowercase			:		Tuple =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  	__lowercase			:		Dict =			torch.zeros(0, device=device, dtype=torch.floataa)
  
  	__lowercase			:		Tuple =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
  	for t in range(max_ep_len):
  		env.render()
  		# add padding
  		__lowercase			:		Tuple =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  		__lowercase			:		Optional[int] =			torch.cat([rewards, torch.zeros(1, device=device)])
  
  		__lowercase			:		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),
  		)
  		__lowercase			:		List[str] =			action
  		__lowercase			:		Optional[int] =			action.detach().cpu().numpy()
  
  		__lowercase ,   __lowercase ,   __lowercase ,   __lowercase			:		Union[str, Any] =			env.step(action)
  
  		__lowercase			:		int =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  		__lowercase			:		List[str] =			torch.cat([states, cur_state], dim=0)
  		__lowercase			:		int =			reward
  
  		__lowercase			:		Optional[Any] =			target_return[0, -1] - (reward / scale)
  		__lowercase			:		Dict =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  		__lowercase			:		Union[str, 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
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __lowercase			:		Any =			{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__lowercase			:		Optional[int] =			[
  	    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'VisualBertForMultipleChoice',
  	    'VisualBertForPreTraining',
  	    'VisualBertForQuestionAnswering',
  	    'VisualBertForRegionToPhraseAlignment',
  	    'VisualBertForVisualReasoning',
  	    'VisualBertLayer',
  	    'VisualBertModel',
  	    'VisualBertPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_visual_bert import (
  		    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    VisualBertForMultipleChoice,
  		    VisualBertForPreTraining,
  		    VisualBertForQuestionAnswering,
  		    VisualBertForRegionToPhraseAlignment,
  		    VisualBertForVisualReasoning,
  		    VisualBertLayer,
  		    VisualBertModel,
  		    VisualBertPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  