



"""simple docstring"""







from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case	:				List[Any]  					=			logging.get_logger(__name__)

class       UpperCamelCase  (					UpperCAmelCase_				):





     """simple docstring"""






     _lowerCamelCase					: Union[str, Any]				   =["input_ids", "attention_mask"]





     def __init__(					self :							Optional[Any]	,      _lowerCamelCase :							Tuple="[PAD]"	,      _lowerCamelCase :							int="[BOS]"	,      _lowerCamelCase :							Any="[EOS]"	,      _lowerCamelCase :							List[str]="[MASK]"	,      _lowerCamelCase :							Any="[CLS]"	,      _lowerCamelCase :							List[str]="[SEP]"	,      _lowerCamelCase :							int=2_0_4_8	,      **_lowerCamelCase :							Any	,      ):
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else pad_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else bos_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else eos_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else mask_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else cls_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else sep_token

          super().__init__(
              pad_token=_lowerCamelCase	,      bos_token=_lowerCamelCase	,      eos_token=_lowerCamelCase	,      mask_token=_lowerCamelCase	,      cls_token=_lowerCamelCase	,      sep_token=_lowerCamelCase	,      model_max_length=_lowerCamelCase	,      **_lowerCamelCase	,      )

          A__				=   2**8  # utf is 8 bits

          # define special tokens dict
          A__				=   {
              self.pad_token: 0,
              self.bos_token: 1,
              self.eos_token: 2,
              self.mask_token: 3,
              self.cls_token: 4,
              self.sep_token: 5,
          }
          A__				=   len(self.special_tokens_encoder )
          A__				=   {v: k for k, v in self.special_tokens_encoder.items()}





     def 						A__  (					self :							List[Any] ):
          A__				=   self.special_tokens_encoder.copy()
          vocab.update(self.added_tokens_encoder )
          for i in range(self._utf_vocab_size ):
               A__				=   chr(_lowerCamelCase )
               A__				=   i + len(self.special_tokens_encoder )
          return vocab





     @property
     def 						A__  (					self :							Optional[int] ):
          return self._utf_vocab_size + self._num_special_tokens





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Any = None	,      _lowerCamelCase :							Union[str, Any] = False ):
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_lowerCamelCase	,      token_ids_a=_lowerCamelCase	,      already_has_special_tokens=_lowerCamelCase )

          # normal case: some special tokens
          if token_ids_a is None:
               return [1] + [0] * len(_lowerCamelCase ) + [1]
          return [1] + ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Optional[int] = None ):
          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          else:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							Optional[Any] ):
          A__				=   [chr(_lowerCamelCase ) for i in text.encode('''utf-8''' )]
          return tokens





     def 						A__  (					self :							int	,      _lowerCamelCase :							Union[str, Any] ):
          if token in self.special_tokens_encoder:
               A__				=   self.special_tokens_encoder[token]
          elif token in self.added_tokens_encoder:
               A__				=   self.added_tokens_encoder[token]
          elif len(_lowerCamelCase ) != 1:
               A__				=   self.unk_token_id
          else:
               A__				=   ord(_lowerCamelCase ) + self._num_special_tokens
          return token_id





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							Dict ):
          if index in self.special_tokens_decoder:
               A__				=   self.special_tokens_decoder[index]
          elif index in self.added_tokens_decoder:
               A__				=   self.added_tokens_decoder[index]
          else:
               A__				=   chr(index - self._num_special_tokens )
          return token





     def 						A__  (					self :							Any	,      _lowerCamelCase :							int ):
          A__				=   B''''''
          for token in tokens:
               if token in self.special_tokens_decoder:
                    A__				=   self.special_tokens_decoder[token].encode('''utf-8''' )
               elif token in self.added_tokens_decoder:
                    A__				=   self.special_tokens_decoder[token].encode('''utf-8''' )
               elif token in self.special_tokens_encoder:
                    A__				=   token.encode('''utf-8''' )
               elif token in self.added_tokens_encoder:
                    A__				=   token.encode('''utf-8''' )
               else:
                    A__				=   bytes([ord(_lowerCamelCase )] )
               bstring += tok_string
          A__				=   bstring.decode('''utf-8'''	,      errors='''replace''' )
          return string





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							int = None ):
          return ()








"""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   a_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a      ):
     # we don't care about the past rewards in this model

     A__				=   states.reshape(1		,							-1		,							model.config.state_dim      )
     A__				=   actions.reshape(1		,							-1		,							model.config.act_dim      )
     A__				=   returns_to_go.reshape(1		,							-1		,							1      )
     A__				=   timesteps.reshape(1		,							-1      )

     if model.config.max_length is not None:
          A__				=   states[:, -model.config.max_length :]
          A__				=   actions[:, -model.config.max_length :]
          A__				=   returns_to_go[:, -model.config.max_length :]
          A__				=   timesteps[:, -model.config.max_length :]

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

     A__	,     A__	,     A__				=   model(
         states=__a		,							actions=__a		,							rewards=__a		,							returns_to_go=__a		,							timesteps=__a		,							attention_mask=__a		,							return_dict=__a		,							)

     return action_preds[0, -1]


# build the environment

__snake_case	:				List[str]  					=			gym.make('Hopper-v3')
__snake_case	:				Optional[Any]  					=			env.observation_space.shape[0]
__snake_case	:				List[str]  					=			env.action_space.shape[0]
__snake_case	:				Optional[int]  					=			1_000
__snake_case	:				Any  					=			'cuda'
__snake_case	:				str  					=			1_000.0  # normalization for rewards/returns
__snake_case	:				str  					=			3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case	:				int  					=			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	:				str  					=			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	:				str  					=			torch.from_numpy(state_mean).to(device=device)
__snake_case	:				str  					=			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__snake_case	:				str  					=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
__snake_case	:				str  					=			model.to(device)
model.eval()

for ep in range(10):
 __snake_case					,  __snake_case	:				Union[str, Any]  					=			0, 0
 __snake_case	:				List[Any]  					=			env.reset()
 __snake_case	:				List[str]  					=			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 __snake_case	:				Union[str, Any]  					=			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 __snake_case	:				List[str]  					=			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 __snake_case	:				str  					=			torch.zeros(0, device=device, dtype=torch.floataa)

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

  __snake_case	:				Any  					=			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	:				List[str]  					=			action
  __snake_case	:				int  					=			action.detach().cpu().numpy()

  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case	:				Union[str, Any]  					=			env.step(action)

  __snake_case	:				Tuple  					=			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  __snake_case	:				Dict  					=			torch.cat([states, cur_state], dim=0)
  __snake_case	:				Optional[Any]  					=			reward

  __snake_case	:				Union[str, Any]  					=			target_return[0, -1] - (reward / scale)
  __snake_case	:				Dict  					=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __snake_case	:				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



