code: 
  
  
  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 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase   )				->   List[Any]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = states.reshape(1				,     -1				,     model.config.state_dim   )
       __UpperCamelCase   = actions.reshape(1				,     -1				,     model.config.act_dim   )
       __UpperCamelCase   = returns_to_go.reshape(1				,     -1				,     1   )
       __UpperCamelCase   = timesteps.reshape(1				,     -1   )
  
       if model.config.max_length is not None:
            __UpperCamelCase   = states[:, -model.config.max_length :]
            __UpperCamelCase   = actions[:, -model.config.max_length :]
            __UpperCamelCase   = returns_to_go[:, -model.config.max_length :]
            __UpperCamelCase   = timesteps[:, -model.config.max_length :]
  
            # pad all tokens to sequence length
            __UpperCamelCase   = torch.cat(
                [torch.zeros(model.config.max_length - states.shape[1]   ), torch.ones(states.shape[1]   )]   )
            __UpperCamelCase   = attention_mask.to(dtype=torch.long				,     device=states.device   ).reshape(1				,     -1   )
            __UpperCamelCase   = 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   = 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   = 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   = 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   = None
  
       __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = model(
           states=_lowercase				,     actions=_lowercase				,     rewards=_lowercase				,     returns_to_go=_lowercase				,     timesteps=_lowercase				,     attention_mask=_lowercase				,     return_dict=_lowercase				,     )
  
       return action_preds[0, -1]
  
  
  # build the environment
  
  __snake_case             =	gym.make('''Hopper-v3''')
  __snake_case             =	env.observation_space.shape[0]
  __snake_case             =	env.action_space.shape[0]
  __snake_case             =	1_0_0_0
  __snake_case             =	'''cuda'''
  __snake_case             =	1_0_0_0.0  # normalization for rewards/returns
  __snake_case             =	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __snake_case             =	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             =	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             =	torch.from_numpy(state_mean).to(device=device)
  __snake_case             =	torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __snake_case             =	DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
  __snake_case             =	model.to(device)
  model.eval()
  
  for ep in range(1_0):
      __snake_case       ,  __snake_case             =	0, 0
      __snake_case             =	env.reset()
      __snake_case             =	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
      __snake_case             =	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
      __snake_case             =	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
      __snake_case             =	torch.zeros(0, device=device, dtype=torch.floataa)
  
      __snake_case             =	torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
      for t in range(max_ep_len):
          env.render()
          # add padding
          __snake_case             =	torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
          __snake_case             =	torch.cat([rewards, torch.zeros(1, device=device)])
  
          __snake_case             =	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             =	action
          __snake_case             =	action.detach().cpu().numpy()
  
          __snake_case       ,  __snake_case       ,  __snake_case       ,  __snake_case             =	env.step(action)
  
          __snake_case             =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
          __snake_case             =	torch.cat([states, cur_state], dim=0)
          __snake_case             =	reward
  
          __snake_case             =	target_return[0, -1] - (reward / scale)
          __snake_case             =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
          __snake_case             =	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: 
  
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
      import torch
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import DeiTImageProcessor
  
  
  
  class   __lowerCamelCase (unittest.TestCase	):
  
  
  
  
  
  
  
         def __init__( self:   List[str],A_:   List[Any],A_:   List[Any]=7,A_:   str=3,A_:   Tuple=18,A_:   Union[str, Any]=30,A_:   Union[str, Any]=400,A_:   List[str]=True,A_:   List[Any]=None,A_:   Tuple=True,A_:   Union[str, Any]=None,A_:   int=True,A_:   Any=[0.5, 0.5, 0.5],A_:   Optional[Any]=[0.5, 0.5, 0.5],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = size if size is not None else {'height': 20, 'width': 20}
              __UpperCamelCase   = crop_size if crop_size is not None else {'height': 18, 'width': 18}
  
              __UpperCamelCase   = parent
              __UpperCamelCase   = batch_size
              __UpperCamelCase   = num_channels
              __UpperCamelCase   = image_size
              __UpperCamelCase   = min_resolution
              __UpperCamelCase   = max_resolution
              __UpperCamelCase   = do_resize
              __UpperCamelCase   = size
              __UpperCamelCase   = do_center_crop
              __UpperCamelCase   = crop_size
              __UpperCamelCase   = do_normalize
              __UpperCamelCase   = image_mean
              __UpperCamelCase   = image_std
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[str]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_center_crop": self.do_center_crop,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }
  
  
  
  @require_torch
  @require_vision
  class   __lowerCamelCase (_a							,			unittest.TestCase	):
         _lowercase       							=    DeiTImageProcessor if is_vision_available() else None
         _lowercase       							=    True
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = DeiTImageProcessingTester(self				)
  
  
  
  
  
  
  
         @property
         def 		snake_case_  ( self:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
              self.assertTrue(hasattr(A_,'do_resize'				)				)
              self.assertTrue(hasattr(A_,'size'				)				)
              self.assertTrue(hasattr(A_,'do_center_crop'				)				)
              self.assertTrue(hasattr(A_,'center_crop'				)				)
              self.assertTrue(hasattr(A_,'do_normalize'				)				)
              self.assertTrue(hasattr(A_,'image_mean'				)				)
              self.assertTrue(hasattr(A_,'image_std'				)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict				)
              self.assertEqual(image_processor.size,{'height': 20, 'width': 20}				)
              self.assertEqual(image_processor.crop_size,{'height': 18, 'width': 18}				)
  
              __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict,size=42,crop_size=84				)
              self.assertEqual(image_processor.size,{'height': 42, 'width': 42}				)
              self.assertEqual(image_processor.crop_size,{'height': 84, 'width': 84}				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              pass
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
              # create random PIL images
              __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_				)
              for image in image_inputs:
                   self.assertIsInstance(A_,Image.Image				)
  
              # Test not batched input
              __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
              self.assertEqual(
                  encoded_images.shape,(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  ),)
  
              # Test batched
              __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
              self.assertEqual(
                  encoded_images.shape,(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  ),)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[str]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
              # create random numpy tensors
              __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,numpify=A_				)
              for image in image_inputs:
                   self.assertIsInstance(A_,np.ndarray				)
  
              # Test not batched input
              __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
              self.assertEqual(
                  encoded_images.shape,(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  ),)
  
              # Test batched
              __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
              self.assertEqual(
                  encoded_images.shape,(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  ),)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,torchify=A_				)
              for image in image_inputs:
                   self.assertIsInstance(A_,torch.Tensor				)
  
              # Test not batched input
              __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
              self.assertEqual(
                  encoded_images.shape,(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  ),)
  
              # Test batched
              __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
              self.assertEqual(
                  encoded_images.shape,(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  ),)
  
  
  