




'''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    snake_case__     (  a       ,      a       ,      a       ,      a       ,      a       ,      a	)			->       List[Any]:





			'''simple docstring'''






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

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

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

			snake_case__    ,			snake_case__    ,			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

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

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

for ep in range(10):
		a__    , a__         =     0, 0
		a__         =     env.reset()
		a__         =     torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
		a__         =     torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
		a__         =     torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
		a__         =     torch.zeros(0, device=device, dtype=torch.floataa)

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

				a__         =     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),
				)
				a__         =     action
				a__         =     action.detach().cpu().numpy()

				a__    , a__    , a__    , a__         =     env.step(action)

				a__         =     torch.from_numpy(state).to(device=device).reshape(1, state_dim)
				a__         =     torch.cat([states, cur_state], dim=0)
				a__         =     reward

				a__         =     target_return[0, -1] - (reward / scale)
				a__         =     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
				a__         =     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






'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xglm import XGLMTokenizer
else:
			lowerCAmelCase__      :   List[str]      	=			None


lowerCAmelCase__      :   Union[str, Any]      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   Tuple      	=			{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__      :   List[str]      	=			{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase__      :   int      	=			{
    "facebook/xglm-564M": 20_48,
}


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE							=	PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE							=	['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE							=	XGLMTokenizer
					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      str="<s>"		,	UpperCAmelCase_       :      List[Any]="</s>"		,	UpperCAmelCase_       :      Dict="</s>"		,	UpperCAmelCase_       :      Optional[Any]="<s>"		,	UpperCAmelCase_       :      List[str]="<unk>"		,	UpperCAmelCase_       :      Union[str, Any]="<pad>"		,	**UpperCAmelCase_       :      List[Any]		,	):







							"""simple docstring"""
							# Compatibility with the original tokenizer
							__UpperCAmelCase	:							Union[str, Any]     =     7
							__UpperCAmelCase	:							Tuple     =     [f"<madeupword{i}>" for i in range(self.num_madeup_words		)]

							__UpperCAmelCase	:							List[Any]     =     kwargs.get("additional_special_tokens"		,	[]		)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    UpperCAmelCase_		,	tokenizer_file=UpperCAmelCase_		,	bos_token=UpperCAmelCase_		,	eos_token=UpperCAmelCase_		,	sep_token=UpperCAmelCase_		,	cls_token=UpperCAmelCase_		,	unk_token=UpperCAmelCase_		,	pad_token=UpperCAmelCase_		,	**UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Dict     =     vocab_file
							__UpperCAmelCase	:							Optional[int]     =     False if not self.vocab_file else True
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""

							if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
							__UpperCAmelCase	:							Any     =     [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     [self.sep_token_id]

							if token_ids_a is None:
									return len(sep + token_ids_a		) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      Optional[str] = None		):







							"""simple docstring"""
							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."		)

							if not os.path.isdir(UpperCAmelCase_		):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
									return
							__UpperCAmelCase	:							str     =     os.path.join(
							    UpperCAmelCase_		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase_		):
									copyfile(self.vocab_file		,	UpperCAmelCase_		)

							return (out_vocab_file,)




