


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(      a__		,a__		,a__		,a__		,a__		,a__):
				# 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=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_  :	Tuple							  =							gym.make('''Hopper-v3''')
snake_case_  :	Optional[Any]							  =							env.observation_space.shape[0]
snake_case_  :	Union[str, Any]							  =							env.action_space.shape[0]
snake_case_  :	Union[str, Any]							  =							10_00
snake_case_  :	Optional[int]							  =							'''cuda'''
snake_case_  :	Tuple							  =							1000.0  # normalization for rewards/returns
snake_case_  :	Optional[Any]							  =							36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case_  :	Optional[int]							  =							np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
snake_case_  :	Tuple							  =							np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
snake_case_  :	Dict							  =							torch.from_numpy(state_mean).to(device=device)
snake_case_  :	List[str]							  =							torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
snake_case_  :	Optional[int]							  =							DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
snake_case_  :	List[Any]							  =							model.to(device)
model.eval()

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

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

														snake_case_  :	str							  =							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_  :	Optional[Any]							  =							action
														snake_case_  :	List[Any]							  =							action.detach().cpu().numpy()

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

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

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



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class   A__							:




						def __init__(				self    :					List[Any] ,			_a    :					List[Any]     )							->   None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =vocab
										_SCREAMING_SNAKE_CASE	     =normalizers.BertNormalizer(
										    clean_text=_a ,			handle_chinese_chars=_a ,			strip_accents=_a ,			lowercase=_a ,			)
										try:
														import rjieba
										except ImportError:
														raise ImportError(
														    '''You need to install rjieba to use RoFormerTokenizer. '''
														    '''See https://pypi.org/project/rjieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =rjieba




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					int ,			_a    :					NormalizedString     )							->   List[NormalizedString]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[]

										# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
										for token, start, end in self.jieba.tokenize(str(_a     ) ,			hmm=_a     ):
														if token in self.vocab:
																		splits.append(normalized_string[start:end]     )
														else:
																		_SCREAMING_SNAKE_CASE	     =self.normalizers.normalize_str(_a     ).split()
																		for token in token_list:
																						if token:
																										_SCREAMING_SNAKE_CASE	     =start + len(_a     )
																										splits.append(normalized_string[start:end]     )
																										_SCREAMING_SNAKE_CASE	     =end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

										return splits




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					PreTokenizedString     )							->   int:


										"""simple docstring"""





										pretok.split(self.jieba_split     )