


'''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 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Optional[Any]			,						_UpperCamelCase					: Optional[int]			,						_UpperCamelCase					: Optional[int]			,						_UpperCamelCase					: Any			,						_UpperCamelCase					: str			,						_UpperCamelCase					: List[str]      )						->    Optional[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__         =  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

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

# Create the decision transformer model
__lowercase:						List[str]  		=   DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
__lowercase:						Any  		=   model.to(device)
model.eval()

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

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

              __lowercase:						Dict  		=   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),
              )
              __lowercase:						Tuple  		=   action
              __lowercase:						Tuple  		=   action.detach().cpu().numpy()

              __lowercase		,__lowercase		,__lowercase		,__lowercase:						Tuple  		=   env.step(action)

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

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





import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowercase_		=							{
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

lowercase_		=							{'''Helsinki-NLP/opus-mt-en-de''': 512}
lowercase_		=							{}





class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: int				    =VOCAB_FILES_NAMES
	lowerCamelCase__		: Any				    =PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__		: Optional[Any]				    =PRETRAINED_INIT_CONFIGURATION
	lowerCamelCase__		: Any				    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__		: Optional[int]				    =["input_ids", "attention_mask"]
	lowerCamelCase__		: Tuple				    =re.compile(">>.+<<" )  # type: re.Pattern






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase="<unk>"				,     lowerCamelCase="</s>"				,     lowerCamelCase="<pad>"				,     lowerCamelCase=512				,     lowerCamelCase = None				,     lowerCamelCase=False				,     **lowerCamelCase				,     )      ->   None:




						"""simple docstring"""






						__magic_name__					:							Tuple		   =							{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
						    source_lang=lowerCamelCase				,     target_lang=lowerCamelCase				,     unk_token=lowerCamelCase				,     eos_token=lowerCamelCase				,     pad_token=lowerCamelCase				,     model_max_length=lowerCamelCase				,     sp_model_kwargs=self.sp_model_kwargs				,     target_vocab_file=lowerCamelCase				,     separate_vocabs=lowerCamelCase				,     **lowerCamelCase				,     )
						assert Path(lowerCamelCase						).exists(), F'''cannot find spm source {source_spm}'''

						__magic_name__					:							Optional[Any]		   =							separate_vocabs
						__magic_name__					:							int		   =							load_json(lowerCamelCase						)
						if self.unk_token not in self.encoder:
											raise KeyError('''<unk> token must be in vocab'''						)
						assert self.pad_token in self.encoder

						if separate_vocabs:
											__magic_name__					:							int		   =							load_json(lowerCamelCase						)
											__magic_name__					:							Tuple		   =							{v: k for k, v in self.target_encoder.items()}
											__magic_name__					:							Optional[Any]		   =							[]
						else:
											__magic_name__					:							Dict		   =							{v: k for k, v in self.encoder.items()}
											__magic_name__					:							list		   =							[k for k in self.encoder if k.startswith('''>>'''						) and k.endswith('''<<'''						)]

						__magic_name__					:							Optional[int]		   =							source_lang
						__magic_name__					:							List[Any]		   =							target_lang
						__magic_name__					:							Tuple		   =							[source_spm, target_spm]

						# load SentencePiece model for pre-processing
						__magic_name__					:							str		   =							load_spm(lowerCamelCase				,     self.sp_model_kwargs						)
						__magic_name__					:							int		   =							load_spm(lowerCamelCase				,     self.sp_model_kwargs						)
						__magic_name__					:							List[str]		   =							self.spm_source
						__magic_name__					:							int		   =							self.encoder

						# Multilingual target side: default to using first supported language code.

						self._setup_normalizer()






	def  lowercase      (	self						)      ->   Union[str, Any]:




						"""simple docstring"""






						try:
											from sacremoses import MosesPunctNormalizer

											__magic_name__					:							List[str]		   =							MosesPunctNormalizer(self.source_lang						).normalize
						except (ImportError, FileNotFoundError):
											warnings.warn('''Recommended: pip install sacremoses.'''						)
											__magic_name__					:							Dict		   =							lambda lowerCamelCase						: x






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						return self.punc_normalizer(lowerCamelCase						) if x else ""






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						return self.current_encoder.get(lowerCamelCase				,     self.current_encoder[self.unk_token]						)






	def  lowercase      (	self				,     lowerCamelCase						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.language_code_re.match(lowerCamelCase						)
						__magic_name__					:							list		   =							[match.group(0						)] if match else []
						return code, self.language_code_re.sub(''''''				,     lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__   , __magic_name__					:							Tuple		   =							self.remove_language_code(lowerCamelCase						)
						__magic_name__					:							Any		   =							self.current_spm.encode(lowerCamelCase				,     out_type=lowerCamelCase						)
						return code + pieces






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						return self.decoder.get(lowerCamelCase				,     self.unk_token						)






	def  lowercase      (	self				,     lowerCamelCase				,     **lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






						return super().batch_decode(lowerCamelCase				,     **lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase				,     **lowerCamelCase						)      ->   Any:




						"""simple docstring"""






						return super().decode(lowerCamelCase				,     **lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.spm_source if self._decode_use_source_tokenizer else self.spm_target
						__magic_name__					:							List[str]		   =							[]
						__magic_name__					:							str		   =							''''''
						for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
																out_string += sp_model.decode_pieces(lowerCamelCase						) + token + " "
																__magic_name__					:							Dict		   =							[]
											else:
																current_sub_tokens.append(lowerCamelCase						)
						out_string += sp_model.decode_pieces(lowerCamelCase						)
						return out_string.strip()






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   List[int]:




						"""simple docstring"""






						if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return token_ids_a + token_ids_a + [self.eos_token_id]






	def  lowercase      (	self						)      ->   List[Any]:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							self.spm_source
						__magic_name__					:							Tuple		   =							self.encoder






	def  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.spm_target
						if self.separate_vocabs:
											__magic_name__					:							List[str]		   =							self.target_encoder






	@property
	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						return len(self.encoder						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = None						)      ->   Tuple[str]:




						"""simple docstring"""






						if not os.path.isdir(lowerCamelCase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
						__magic_name__					:							Any		   =							[]

						if self.separate_vocabs:
											__magic_name__					:							Optional[Any]		   =							os.path.join(
											    lowerCamelCase				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']				,     )
											__magic_name__					:							Dict		   =							os.path.join(
											    lowerCamelCase				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']				,     )
											save_json(self.encoder				,     lowerCamelCase						)
											save_json(self.target_encoder				,     lowerCamelCase						)
											saved_files.append(lowerCamelCase						)
											saved_files.append(lowerCamelCase						)
						else:
											__magic_name__					:							List[str]		   =							os.path.join(
											    lowerCamelCase				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']						)
											save_json(self.encoder				,     lowerCamelCase						)
											saved_files.append(lowerCamelCase						)

						for spm_save_filename, spm_orig_path, spm_model in zip(
						    [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']]				,     self.spm_files				,     [self.spm_source, self.spm_target]				,     ):
											__magic_name__					:							List[Any]		   =							os.path.join(
											    lowerCamelCase				,     (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename						)
											if os.path.abspath(lowerCamelCase						) != os.path.abspath(lowerCamelCase						) and os.path.isfile(lowerCamelCase						):
																copyfile(lowerCamelCase				,     lowerCamelCase						)
																saved_files.append(lowerCamelCase						)
											elif not os.path.isfile(lowerCamelCase						):
																with open(lowerCamelCase				,     '''wb'''						) as fi:
																					__magic_name__					:							int		   =							spm_model.serialized_model_proto()
																					fi.write(lowerCamelCase						)
																saved_files.append(lowerCamelCase						)

						return tuple(lowerCamelCase						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						return self.get_src_vocab()






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						return dict(self.encoder				,     **self.added_tokens_encoder						)






	def  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						return dict(self.target_encoder				,     **self.added_tokens_decoder						)






	def __getstate__(	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							self.__dict__.copy()
						state.update(
						    {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}						)
						return state






	def __setstate__(	self				,     lowerCamelCase						)      ->   None:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							d

						# for backward compatibility
						if not hasattr(self				,     '''sp_model_kwargs'''						):
											__magic_name__					:							Dict		   =							{}

						__magic_name__   , __magic_name__					:							Optional[int]		   =							(load_spm(lowerCamelCase				,     self.sp_model_kwargs						) for f in self.spm_files)
						__magic_name__					:							str		   =							self.spm_source
						self._setup_normalizer()






	def  lowercase      (	self				,     *lowerCamelCase				,     **lowerCamelCase						)      ->   Optional[Any]:




						"""simple docstring"""






						return 1






	def  lowercase      (	self				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							set(self.all_special_ids						)  # call it once instead of inside list comp
						all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special
						return [1 if x in all_special_ids else 0 for x in seq]






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = None				,     lowerCamelCase = False						)      ->   List[int]:




						"""simple docstring"""






						if already_has_special_tokens:
											return self._special_token_mask(lowerCamelCase						)
						elif token_ids_a is None:
											return self._special_token_mask(lowerCamelCase						) + [1]
						else:
											return self._special_token_mask(token_ids_a + token_ids_a						) + [1]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->sentencepiece.SentencePieceProcessor:






					"""simple docstring"""






					__magic_name__					:							int		   =							sentencepiece.SentencePieceProcessor(**UpperCAmelCase       )
					spm.Load(UpperCAmelCase       )
					return spm







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->None:






					"""simple docstring"""






					with open(UpperCAmelCase,	'''w'''       ) as f:
										json.dump(UpperCAmelCase,	UpperCAmelCase,	indent=2       )







def  lowerCAmelCase  (	UpperCAmelCase       )  ->Union[Dict, List]:






					"""simple docstring"""






					with open(UpperCAmelCase,	'''r'''       ) as f:
										return json.load(UpperCAmelCase       )



