



"""simple docstring"""







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


__snake_case	:				List[Any]  					=			logging.get_logger(__name__)

__snake_case	:				Optional[int]  					=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__snake_case	:				List[Any]  					=			{
    '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'},
}

__snake_case	:				Dict  					=			{
    'openai-gpt': 512,
}



def   a_	(			__a      ):
     A__				=   text.strip()
     if not text:
          return []
     A__				=   text.split()
     return tokens

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def __init__(					self :							Dict	,      _lowerCamelCase :							Optional[int]=True	,      _lowerCamelCase :							List[Any]=None	,      _lowerCamelCase :							int=True	,      _lowerCamelCase :							Optional[int]=None	,      _lowerCamelCase :							str=True	,      ):
          if never_split is None:
               A__				=   []
          A__				=   do_lower_case
          A__				=   set(_lowerCamelCase )
          A__				=   tokenize_chinese_chars
          A__				=   strip_accents
          A__				=   do_split_on_punc





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Union[str, Any]=None ):
          A__				=   self.never_split.union(set(_lowerCamelCase ) ) if never_split else self.never_split
          A__				=   self._clean_text(_lowerCamelCase )

          # 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:
               A__				=   self._tokenize_chinese_chars(_lowerCamelCase )
          # prevents treating the same character with different unicode codepoints as different characters
          A__				=   unicodedata.normalize('''NFC'''	,      _lowerCamelCase )
          A__				=   whitespace_tokenize(_lowerCamelCase )
          A__				=   []
          for token in orig_tokens:
               if token not in never_split:
                    if self.do_lower_case:
                         A__				=   token.lower()
                         if self.strip_accents is not False:
                              A__				=   self._run_strip_accents(_lowerCamelCase )
                    elif self.strip_accents:
                         A__				=   self._run_strip_accents(_lowerCamelCase )
               split_tokens.extend(self._run_split_on_punc(_lowerCamelCase	,      _lowerCamelCase ) )

          A__				=   whitespace_tokenize(''' '''.join(_lowerCamelCase ) )
          return output_tokens





     def 						A__  (					self :							int	,      _lowerCamelCase :							Any ):
          A__				=   unicodedata.normalize('''NFD'''	,      _lowerCamelCase )
          A__				=   []
          for char in text:
               A__				=   unicodedata.category(_lowerCamelCase )
               if cat == "Mn":
                    continue
               output.append(_lowerCamelCase )
          return "".join(_lowerCamelCase )





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							int	,      _lowerCamelCase :							List[Any]=None ):
          if not self.do_split_on_punc or (never_split is not None and text in never_split):
               return [text]
          A__				=   list(_lowerCamelCase )
          A__				=   0
          A__				=   True
          A__				=   []
          while i < len(_lowerCamelCase ):
               A__				=   chars[i]
               if _is_punctuation(_lowerCamelCase ):
                    output.append([char] )
                    A__				=   True
               else:
                    if start_new_word:
                         output.append([] )
                    A__				=   False
                    output[-1].append(_lowerCamelCase )
               i += 1

          return ["".join(_lowerCamelCase ) for x in output]





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							List[Any] ):
          A__				=   []
          for char in text:
               A__				=   ord(_lowerCamelCase )
               if self._is_chinese_char(_lowerCamelCase ):
                    output.append(''' ''' )
                    output.append(_lowerCamelCase )
                    output.append(''' ''' )
               else:
                    output.append(_lowerCamelCase )
          return "".join(_lowerCamelCase )





     def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							Optional[Any] ):
          # This defines a "chinese character" as anything in the CJK Unicode block:
          #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
          #
          # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
          # despite its name. The modern Korean Hangul alphabet is a different block,
          # as is Japanese Hiragana and Katakana. Those alphabets are used to write
          # space-separated words, so they are not treated specially and handled
          # like the all of the other languages.
          if (
              (cp >= 0x4E00 and cp <= 0x9FFF)
              or (cp >= 0x3400 and cp <= 0x4DBF)  #
              or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
              or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
              or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
              or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
              or (cp >= 0xF900 and cp <= 0xFAFF)
              or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
          ):  #
               return True

          return False





     def 						A__  (					self :							Any	,      _lowerCamelCase :							str ):
          A__				=   []
          for char in text:
               A__				=   ord(_lowerCamelCase )
               if cp == 0 or cp == 0xFFFD or _is_control(_lowerCamelCase ):
                    continue
               if _is_whitespace(_lowerCamelCase ):
                    output.append(''' ''' )
               else:
                    output.append(_lowerCamelCase )
          return "".join(_lowerCamelCase )



def   a_	(			__a      ):
     A__				=   set()
     A__				=   word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)      )
          A__				=   char
     return pairs



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

class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: Any				   =VOCAB_FILES_NAMES
     _lowerCamelCase					: Any				   =PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase					: Any				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase					: Tuple				   =["input_ids", "attention_mask"]





     def __init__(					self :							Union[str, Any]	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							List[str]="<unk>"	,      **_lowerCamelCase :							Optional[int] ):
          super().__init__(unk_token=_lowerCamelCase	,      **_lowerCamelCase )

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

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

          with open(_lowerCamelCase	,      encoding='''utf-8''' ) as vocab_handle:
               A__				=   json.load(_lowerCamelCase )
          A__				=   {v: k for k, v in self.encoder.items()}
          with open(_lowerCamelCase	,      encoding='''utf-8''' ) as merges_handle:
               A__				=   merges_handle.read().split('''\n''' )[1:-1]
          A__				=   [tuple(merge.split() ) for merge in merges]
          A__				=   dict(zip(_lowerCamelCase	,      range(len(_lowerCamelCase ) ) ) )
          A__				=   {}





     @property
     def 						A__  (					self :							List[str] ):
          return True





     @property
     def 						A__  (					self :							Tuple ):
          return len(self.encoder )





     def 						A__  (					self :							Union[str, Any] ):
          return dict(self.encoder	,      **self.added_tokens_encoder )





     def 						A__  (					self :							int	,      _lowerCamelCase :							Any ):
          A__				=   tuple(token[:-1] ) + (token[-1] + '''</w>''',)
          if token in self.cache:
               return self.cache[token]
          A__				=   get_pairs(_lowerCamelCase )

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

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

                    if word[i] == first and i < len(_lowerCamelCase ) - 1 and word[i + 1] == second:
                         new_word.append(first + second )
                         i += 2
                    else:
                         new_word.append(word[i] )
                         i += 1
               A__				=   tuple(_lowerCamelCase )
               A__				=   new_word
               if len(_lowerCamelCase ) == 1:
                    break
               else:
                    A__				=   get_pairs(_lowerCamelCase )
          A__				=   ''' '''.join(_lowerCamelCase )
          if word == "\n  </w>":
               A__				=   '''\n</w>'''
          A__				=   word
          return word





     def 						A__  (					self :							int	,      _lowerCamelCase :							int ):
          A__				=   []
          if self.fix_text is None:
               # Using BERT's BasicTokenizer
               A__				=   self.nlp.tokenize(_lowerCamelCase )
               for token in text:
                    split_tokens.extend(list(self.bpe(_lowerCamelCase ).split(''' ''' ) ) )
          else:
               # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
               A__				=   self.nlp(text_standardize(self.fix_text(_lowerCamelCase ) ) )
               for token in text:
                    split_tokens.extend(list(self.bpe(token.text.lower() ).split(''' ''' ) ) )
          return split_tokens





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Optional[Any] ):
          return self.encoder.get(_lowerCamelCase	,      self.encoder.get(self.unk_token ) )





     def 						A__  (					self :							str	,      _lowerCamelCase :							Tuple ):
          return self.decoder.get(_lowerCamelCase	,      self.unk_token )





     def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							List[str] ):
          A__				=   ''''''.join(_lowerCamelCase ).replace('''</w>'''	,      ''' ''' ).strip()
          return out_string





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Optional[str] = None ):
          if not os.path.isdir(_lowerCamelCase ):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
               return
          A__				=   os.path.join(
              _lowerCamelCase	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
          A__				=   os.path.join(
              _lowerCamelCase	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

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

          A__				=   0
          with open(_lowerCamelCase	,      '''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 _lowerCamelCase : 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!''' )
                         A__				=   token_index
                    writer.write(''' '''.join(_lowerCamelCase ) + '''\n''' )
                    index += 1

          return vocab_file, merge_file








"""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   a_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a      ):
     # we don't care about the past rewards in this model

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

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

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

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

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

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

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

  __snake_case	:				Any  					=			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	:				List[str]  					=			action
  __snake_case	:				int  					=			action.detach().cpu().numpy()

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

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

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



