

import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_lowerCamelCase							=logging.get_logger(__name__)

_lowerCamelCase							={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowerCamelCase							={
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}

_lowerCamelCase							={
    "openai-gpt": 5_12,
}







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=text.strip()
     if not text:
          return []
     SCREAMING_SNAKE_CASE 		=text.split()
     return tokens



class 						a_			(				A__	):



      """simple docstring"""







      def __init__( self	:				List[str]     ,snake_case	:				Optional[Any]=True     ,snake_case	:				List[str]=None     ,snake_case	:				int=True     ,snake_case	:				Tuple=None     ,snake_case	:				Optional[int]=True     ,):

           if never_split is None:
                SCREAMING_SNAKE_CASE 		=[]
           SCREAMING_SNAKE_CASE 		=do_lower_case
           SCREAMING_SNAKE_CASE 		=set(snake_case							)
           SCREAMING_SNAKE_CASE 		=tokenize_chinese_chars
           SCREAMING_SNAKE_CASE 		=strip_accents
           SCREAMING_SNAKE_CASE 		=do_split_on_punc
      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				Optional[int]     ,snake_case	:				int=None							):
           SCREAMING_SNAKE_CASE 		=self.never_split.union(set(snake_case							)							) if never_split else self.never_split
           SCREAMING_SNAKE_CASE 		=self._clean_text(snake_case							)

           # This was added on November 1st, 2018 for the multilingual and Chinese
           # models. This is also applied to the English models now, but it doesn't
           # matter since the English models were not trained on any Chinese data
           # and generally don't have any Chinese data in them (there are Chinese
           # characters in the vocabulary because Wikipedia does have some Chinese
           # words in the English Wikipedia.).
           if self.tokenize_chinese_chars:
                SCREAMING_SNAKE_CASE 		=self._tokenize_chinese_chars(snake_case							)
           # prevents treating the same character with different unicode codepoints as different characters
           SCREAMING_SNAKE_CASE 		=unicodedata.normalize('NFC'     ,snake_case							)
           SCREAMING_SNAKE_CASE 		=whitespace_tokenize(snake_case							)
           SCREAMING_SNAKE_CASE 		=[]
           for token in orig_tokens:
                if token not in never_split:
                     if self.do_lower_case:
                          SCREAMING_SNAKE_CASE 		=token.lower()
                          if self.strip_accents is not False:
                               SCREAMING_SNAKE_CASE 		=self._run_strip_accents(snake_case							)
                     elif self.strip_accents:
                          SCREAMING_SNAKE_CASE 		=self._run_strip_accents(snake_case							)
                split_tokens.extend(self._run_split_on_punc(snake_case     ,snake_case							)							)

           SCREAMING_SNAKE_CASE 		=whitespace_tokenize(' '.join(snake_case							)							)
           return output_tokens
      def 			_lowerCAmelCase	( self	:				List[str]     ,snake_case	:				Dict							):
           SCREAMING_SNAKE_CASE 		=unicodedata.normalize('NFD'     ,snake_case							)
           SCREAMING_SNAKE_CASE 		=[]
           for char in text:
                SCREAMING_SNAKE_CASE 		=unicodedata.category(snake_case							)
                if cat == "Mn":
                     continue
                output.append(snake_case							)
           return "".join(snake_case							)
      def 			_lowerCAmelCase	( self	:				List[Any]     ,snake_case	:				str     ,snake_case	:				Optional[Any]=None							):

           if not self.do_split_on_punc or (never_split is not None and text in never_split):
                return [text]
           SCREAMING_SNAKE_CASE 		=list(snake_case							)
           SCREAMING_SNAKE_CASE 		=0
           SCREAMING_SNAKE_CASE 		=True
           SCREAMING_SNAKE_CASE 		=[]
           while i < len(snake_case							):
                SCREAMING_SNAKE_CASE 		=chars[i]
                if _is_punctuation(snake_case							):
                     output.append([char]							)
                     SCREAMING_SNAKE_CASE 		=True
                else:
                     if start_new_word:
                          output.append([]							)
                     SCREAMING_SNAKE_CASE 		=False
                     output[-1].append(snake_case							)
                i += 1

           return ["".join(snake_case							) for x in output]
      def 			_lowerCAmelCase	( self	:				Union[str, Any]     ,snake_case	:				Dict							):
           SCREAMING_SNAKE_CASE 		=[]
           for char in text:
                SCREAMING_SNAKE_CASE 		=ord(snake_case							)
                if self._is_chinese_char(snake_case							):
                     output.append(' '							)
                     output.append(snake_case							)
                     output.append(' '							)
                else:
                     output.append(snake_case							)
           return "".join(snake_case							)
      def 			_lowerCAmelCase	( self	:				Optional[Any]     ,snake_case	:				Tuple							):

           if (
               (cp >= 0X4_E00 and cp <= 0X9_FFF)
               or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
               or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
               or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
               or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
               or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
               or (cp >= 0XF_900 and cp <= 0XF_AFF)
               or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
           ):  #
                return True

           return False


      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				str							):
           SCREAMING_SNAKE_CASE 		=[]
           for char in text:
                SCREAMING_SNAKE_CASE 		=ord(snake_case							)
                if cp == 0 or cp == 0XF_FFD or _is_control(snake_case							):
                     continue
                if _is_whitespace(snake_case							):
                     output.append(' '							)
                else:
                     output.append(snake_case							)
           return "".join(snake_case							)







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=set()
     SCREAMING_SNAKE_CASE 		=word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)     )
          SCREAMING_SNAKE_CASE 		=char
     return pairs







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=text.replace('—',       '-'     )
     SCREAMING_SNAKE_CASE 		=text.replace('–',       '-'     )
     SCREAMING_SNAKE_CASE 		=text.replace('―',       '-'     )
     SCREAMING_SNAKE_CASE 		=text.replace('…',       '...'     )
     SCREAMING_SNAKE_CASE 		=text.replace('´',       '\''     )
     SCREAMING_SNAKE_CASE 		=re.sub(r'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)',       r' \1 ',       lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=re.sub(r'\s*\n\s*',       ' \n ',       lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=re.sub(r'[^\S\n]+',       ' ',       lowerCAmelCase_     )
     return text.strip()



class 						a_			(				A__	):



      """simple docstring"""







      __UpperCAmelCase     						= VOCAB_FILES_NAMES
      __UpperCAmelCase     						= PRETRAINED_VOCAB_FILES_MAP
      __UpperCAmelCase     						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCAmelCase     						= ['input_ids', 'attention_mask']
      def __init__( self	:				int     ,snake_case	:				List[Any]     ,snake_case	:				Optional[Any]     ,snake_case	:				int="<unk>"     ,**snake_case	:				int							):
           super().__init__(unk_token=snake_case     ,**snake_case							)

           try:
                import ftfy
                from spacy.lang.en import English

                SCREAMING_SNAKE_CASE 		=English()
                SCREAMING_SNAKE_CASE 		=_nlp.tokenizer
                SCREAMING_SNAKE_CASE 		=ftfy.fix_text
           except ImportError:
                logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'							)
                SCREAMING_SNAKE_CASE 		=BasicTokenizer(do_lower_case=snake_case							)
                SCREAMING_SNAKE_CASE 		=None

           with open(snake_case     ,encoding='utf-8'							) as vocab_handle:
                SCREAMING_SNAKE_CASE 		=json.load(snake_case							)
           SCREAMING_SNAKE_CASE 		={v: k for k, v in self.encoder.items()}
           with open(snake_case     ,encoding='utf-8'							) as merges_handle:
                SCREAMING_SNAKE_CASE 		=merges_handle.read().split('\n'							)[1:-1]
           SCREAMING_SNAKE_CASE 		=[tuple(merge.split()							) for merge in merges]
           SCREAMING_SNAKE_CASE 		=dict(zip(snake_case     ,range(len(snake_case							)							)							)							)
           SCREAMING_SNAKE_CASE 		={}
      @property
      def 			_lowerCAmelCase	( self	:				int							):
           return True
      @property
      def 			_lowerCAmelCase	( self	:				str							):
           return len(self.encoder							)
      def 			_lowerCAmelCase	( self	:				int							):
           return dict(self.encoder     ,**self.added_tokens_encoder							)
      def 			_lowerCAmelCase	( self	:				Optional[int]     ,snake_case	:				List[Any]							):
           SCREAMING_SNAKE_CASE 		=tuple(token[:-1]							) + (token[-1] + '''</w>''',)
           if token in self.cache:
                return self.cache[token]
           SCREAMING_SNAKE_CASE 		=get_pairs(snake_case							)

           if not pairs:
                return token + "</w>"

           while True:
                SCREAMING_SNAKE_CASE 		=min(snake_case     ,key=lambda snake_case							: self.bpe_ranks.get(snake_case     ,float('inf'							)							)							)
                if bigram not in self.bpe_ranks:
                     break
                SCREAMING_SNAKE_CASE 		=bigram
                SCREAMING_SNAKE_CASE 		=[]
                SCREAMING_SNAKE_CASE 		=0
                while i < len(snake_case							):
                     try:
                          SCREAMING_SNAKE_CASE 		=word.index(snake_case     ,snake_case							)
                     except ValueError:
                          new_word.extend(word[i:]							)
                          break
                     else:
                          new_word.extend(word[i:j]							)
                          SCREAMING_SNAKE_CASE 		=j

                     if word[i] == first and i < len(snake_case							) - 1 and word[i + 1] == second:
                          new_word.append(first + second							)
                          i += 2
                     else:
                          new_word.append(word[i]							)
                          i += 1
                SCREAMING_SNAKE_CASE 		=tuple(snake_case							)
                SCREAMING_SNAKE_CASE 		=new_word
                if len(snake_case							) == 1:
                     break
                else:
                     SCREAMING_SNAKE_CASE 		=get_pairs(snake_case							)
           SCREAMING_SNAKE_CASE 		=''' '''.join(snake_case							)
           if word == "\n  </w>":
                SCREAMING_SNAKE_CASE 		='''\n</w>'''
           SCREAMING_SNAKE_CASE 		=word
           return word
      def 			_lowerCAmelCase	( self	:				Optional[int]     ,snake_case	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=[]
           if self.fix_text is None:
                # Using BERT's BasicTokenizer
                SCREAMING_SNAKE_CASE 		=self.nlp.tokenize(snake_case							)
                for token in text:
                     split_tokens.extend(list(self.bpe(snake_case							).split(' '							)							)							)
           else:
                # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
                SCREAMING_SNAKE_CASE 		=self.nlp(text_standardize(self.fix_text(snake_case							)							)							)
                for token in text:
                     split_tokens.extend(list(self.bpe(token.text.lower()							).split(' '							)							)							)
           return split_tokens
      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				List[str]							):
           return self.encoder.get(snake_case     ,self.encoder.get(self.unk_token							)							)
      def 			_lowerCAmelCase	( self	:				Any     ,snake_case	:				Optional[int]							):
           return self.decoder.get(snake_case     ,self.unk_token							)
      def 			_lowerCAmelCase	( self	:				List[Any]     ,snake_case	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		=''''''.join(snake_case							).replace('</w>'     ,' '							).strip()
           return out_string


      def 			_lowerCAmelCase	( self	:				Union[str, Any]     ,snake_case	:				Union[str, Any]     ,snake_case	:				Optional[int] = None							):

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

           with open(snake_case     ,'w'     ,encoding='utf-8'							) as f:
                f.write(json.dumps(self.encoder     ,indent=2     ,sort_keys=snake_case     ,ensure_ascii=snake_case							) + '\n'							)

           SCREAMING_SNAKE_CASE 		=0
           with open(snake_case     ,'w'     ,encoding='utf-8'							) as writer:
                writer.write('#version: 0.2\n'							)
                for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,key=lambda snake_case							: kv[1]							):
                     if index != token_index:
                          logger.warning(
                              f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                              ' Please check that the tokenizer is not corrupted!'							)
                          SCREAMING_SNAKE_CASE 		=token_index
                     writer.write(' '.join(snake_case							) + '\n'							)
                     index += 1

           return vocab_file, merge_file


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							)		->	int:
       # we don't care about the past rewards in this model

       A_							:       Dict	      =				states.reshape(1  ,   -1  ,   model.config.state_dim							)
       A_							:       Dict	      =				actions.reshape(1  ,   -1  ,   model.config.act_dim							)
       A_							:       Dict	      =				returns_to_go.reshape(1  ,   -1  ,   1							)
       A_							:       Tuple	      =				timesteps.reshape(1  ,   -1							)

       if model.config.max_length is not None:
              A_							:       int	      =				states[:, -model.config.max_length :]
              A_							:       Optional[int]	      =				actions[:, -model.config.max_length :]
              A_							:       Any	      =				returns_to_go[:, -model.config.max_length :]
              A_							:       Tuple	      =				timesteps[:, -model.config.max_length :]

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

       A_      ,							A_      ,							A_							:       List[str]	      =				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

_lowerCAmelCase        =       gym.make('Hopper-v3')
_lowerCAmelCase        =       env.observation_space.shape[0]
_lowerCAmelCase        =       env.action_space.shape[0]
_lowerCAmelCase        =       1_0_0_0
_lowerCAmelCase        =       'cuda'
_lowerCAmelCase        =       1000.0  # normalization for rewards/returns
_lowerCAmelCase        =       3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase        =       np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_lowerCAmelCase        =       np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_lowerCAmelCase        =       torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase        =       torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
    _lowerCAmelCase  ,			_lowerCAmelCase        =       0, 0
    _lowerCAmelCase        =       env.reset()
    _lowerCAmelCase        =       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
    _lowerCAmelCase        =       torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
    _lowerCAmelCase        =       torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
    _lowerCAmelCase        =       torch.zeros(0, device=device, dtype=torch.floataa)

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

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

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

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

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


