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   )				->   Union[str, 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=_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
  
  __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_000.0  # 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 argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
       __UpperCamelCase   = GPTNeoConfig(
           hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
       print(f'''Building PyTorch model from configuration: {config}'''   )
       __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )
  
       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
       model.save_pretrained(_lowercase   )
  
  
  if __name__ == "__main__":
      __snake_case             =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __snake_case             =	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  