
"""simple docstring"""




from typing import Dict, List, Optional, Tuple

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


snake_case					  =						logging.get_logger(__name__)




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							["input_ids", "attention_mask"]


				def __init__(							self						,      lowercase__="[PAD]"						,      lowercase__="[BOS]"						,      lowercase__="[EOS]"						,      lowercase__="[MASK]"						,      lowercase__="[CLS]"						,      lowercase__="[SEP]"						,      lowercase__=2048						,      **lowercase__						,      )       ->       None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else pad_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else bos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else eos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else mask_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else cls_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else sep_token

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

									SCREAMING_SNAKE_CASE           =      2**8  # utf is 8 bits

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


				def 				A   (							self     )       ->       Dict[str, int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder     )
									for i in range(self._utf_vocab_size     ):
														SCREAMING_SNAKE_CASE           =      chr(lowercase__     )
														SCREAMING_SNAKE_CASE           =      i + len(self.special_tokens_encoder     )
									return vocab


				@property
				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									return self._utf_vocab_size + self._num_special_tokens


				def 				A   (							self						,      lowercase__						,      lowercase__ = None						,      lowercase__ = False     )       ->       List[int]:
									"""simple docstring"""







									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowercase__						,      token_ids_a=lowercase__						,      already_has_special_tokens=lowercase__     )

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


				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       List[int]:
									"""simple docstring"""







									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						,      lowercase__     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [chr(lowercase__     ) for i in text.encode('utf-8'     )]
									return tokens


				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									if token in self.special_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_encoder[token]
									elif len(lowercase__     ) != 1:
														SCREAMING_SNAKE_CASE           =      self.unk_token_id
									else:
														SCREAMING_SNAKE_CASE           =      ord(lowercase__     ) + self._num_special_tokens
									return token_id


				def 				A   (							self						,      lowercase__     )       ->       int:
									"""simple docstring"""







									if index in self.special_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_decoder[index]
									else:
														SCREAMING_SNAKE_CASE           =      chr(index - self._num_special_tokens     )
									return token


				def 				A   (							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      b''
									for token in tokens:
														if token in self.special_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.added_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.special_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														elif token in self.added_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														else:
																			SCREAMING_SNAKE_CASE           =      bytes([ord(lowercase__     )]     )
														bstring += tok_string
									SCREAMING_SNAKE_CASE           =      bstring.decode('utf-8'						,      errors='replace'     )
									return string




				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       Tuple[str]:
									"""simple docstring"""







									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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# we don't care about the past rewards in this model

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

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

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

					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      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

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(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
snake_case					  =						np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
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
