code: 
  
  import json
  import os
  from functools import lru_cache
  from typing import TYPE_CHECKING, List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation
  
  __A   :     str      =			logging.get_logger(__name__)
  
  __A   :     int      =			{
      '''vocab_file''': '''vocab.json''',
      '''merges_file''': '''merges.txt''',
  }
  
  __A   :     Optional[Any]      =			{
      '''vocab_file''': {
          '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
          '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
          '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
          '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
          '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
      },
      '''merges_file''': {
          '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
          '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
          '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
          '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
          '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
      },
  }
  
  __A   :     Any      =			{
      '''gpt2''': 1024,
      '''gpt2-medium''': 1024,
      '''gpt2-large''': 1024,
      '''gpt2-xl''': 1024,
      '''distilgpt2''': 1024,
  }
  @lru_cache()
  def 		SCREAMING_SNAKE_CASE__				(							)					-> Dict:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Union[str, Any]    			=      (
          list(range(ord('!'				),      ord('~'				) + 1				)				) + list(range(ord('¡'				),      ord('¬'				) + 1				)				) + list(range(ord('®'				),      ord('ÿ'				) + 1				)				)
      )
      lowerCAmelCase				:			Optional[int]    			=      bs[:]
      lowerCAmelCase				:			Dict    			=      0
      for b in range(2**8				):
          if b not in bs:
              bs.append(_UpperCAmelCase				)
              cs.append(2**8 + n				)
              n += 1
      lowerCAmelCase				:			Dict    			=      [chr(_UpperCAmelCase				) for n in cs]
      return dict(zip(_UpperCAmelCase,      _UpperCAmelCase				)				)
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> Any:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      set()
      lowerCAmelCase				:			Optional[int]    			=      word[0]
      for char in word[1:]:
          pairs.add((prev_char, char)				)
          lowerCAmelCase				:			str    			=      char
      return pairs
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase__ ):
      lowerCAmelCase_							:		Optional[int]						=							VOCAB_FILES_NAMES
      lowerCAmelCase_							:		int						=							PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase_							:		Optional[int]						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase_							:		Any						=							["""input_ids""", """attention_mask"""]
  
  
  
  
  
  
      def __init__(	self			:    Optional[int]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Any="replace"    ,				UpperCAmelCase_			:    List[Any]="<|endoftext|>"    ,				UpperCAmelCase_			:    int="<|endoftext|>"    ,				UpperCAmelCase_			:    Optional[int]="<|endoftext|>"    ,				UpperCAmelCase_			:    int=None    ,				UpperCAmelCase_			:    Union[str, Any]=False    ,				UpperCAmelCase_			:    Any=False    ,				**UpperCAmelCase_			:    Union[str, Any]    ,				):
          lowerCAmelCase				:			Dict    			=      AddedToken(lowerCAmelCase_    ,				lstrip=lowerCAmelCase_    ,				rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_    ,				lowerCAmelCase_    ) else bos_token
          lowerCAmelCase				:			Optional[Any]    			=      AddedToken(lowerCAmelCase_    ,				lstrip=lowerCAmelCase_    ,				rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_    ,				lowerCAmelCase_    ) else eos_token
          lowerCAmelCase				:			int    			=      AddedToken(lowerCAmelCase_    ,				lstrip=lowerCAmelCase_    ,				rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_    ,				lowerCAmelCase_    ) else unk_token
          lowerCAmelCase				:			List[Any]    			=      AddedToken(lowerCAmelCase_    ,				lstrip=lowerCAmelCase_    ,				rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_    ,				lowerCAmelCase_    ) else pad_token
          super().__init__(
              errors=lowerCAmelCase_    ,				unk_token=lowerCAmelCase_    ,				bos_token=lowerCAmelCase_    ,				eos_token=lowerCAmelCase_    ,				pad_token=lowerCAmelCase_    ,				add_prefix_space=lowerCAmelCase_    ,				add_bos_token=lowerCAmelCase_    ,				**lowerCAmelCase_    ,				)
          lowerCAmelCase				:			Tuple    			=      add_bos_token
  
          with open(lowerCAmelCase_    ,				encoding='utf-8'    ) as vocab_handle:
              lowerCAmelCase				:			List[str]    			=      json.load(lowerCAmelCase_    )
          lowerCAmelCase				:			Dict    			=      {v: k for k, v in self.encoder.items()}
          lowerCAmelCase				:			Any    			=      errors  # how to handle errors in decoding
          lowerCAmelCase				:			List[str]    			=      bytes_to_unicode()
          lowerCAmelCase				:			str    			=      {v: k for k, v in self.byte_encoder.items()}
          with open(lowerCAmelCase_    ,				encoding='utf-8'    ) as merges_handle:
              lowerCAmelCase				:			List[Any]    			=      merges_handle.read().split('\n'    )[1:-1]
          lowerCAmelCase				:			Optional[int]    			=      [tuple(merge.split()    ) for merge in bpe_merges]
          lowerCAmelCase				:			Optional[int]    			=      dict(zip(lowerCAmelCase_    ,				range(len(lowerCAmelCase_    )    )    )    )
          lowerCAmelCase				:			Tuple    			=      {}
          lowerCAmelCase				:			str    			=      add_prefix_space
  
          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          lowerCAmelCase				:			List[str]    			=      re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'    )
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Tuple    ):
          return len(self.encoder    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          return dict(self.encoder    ,				**self.added_tokens_encoder    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]    ):
  
  
  
  
  
  
          if token in self.cache:
              return self.cache[token]
          lowerCAmelCase				:			str    			=      tuple(lowerCAmelCase_    )
          lowerCAmelCase				:			str    			=      get_pairs(lowerCAmelCase_    )
  
          if not pairs:
              return token
  
          while True:
              lowerCAmelCase				:			Any    			=      min(lowerCAmelCase_    ,				key=lambda UpperCAmelCase_    : self.bpe_ranks.get(lowerCAmelCase_    ,				float('inf'    )    )    )
              if bigram not in self.bpe_ranks:
                  break
              lowerCAmelCase			,  lowerCAmelCase				:			int    			=      bigram
              lowerCAmelCase				:			int    			=      []
              lowerCAmelCase				:			Optional[Any]    			=      0
              while i < len(lowerCAmelCase_    ):
                  try:
                      lowerCAmelCase				:			Union[str, Any]    			=      word.index(lowerCAmelCase_    ,				lowerCAmelCase_    )
                  except ValueError:
                      new_word.extend(word[i:]    )
                      break
                  else:
                      new_word.extend(word[i:j]    )
                      lowerCAmelCase				:			Optional[int]    			=      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
              lowerCAmelCase				:			List[Any]    			=      tuple(lowerCAmelCase_    )
              lowerCAmelCase				:			List[Any]    			=      new_word
              if len(lowerCAmelCase_    ) == 1:
                  break
              else:
                  lowerCAmelCase				:			int    			=      get_pairs(lowerCAmelCase_    )
          lowerCAmelCase				:			str    			=      ' '.join(lowerCAmelCase_    )
          lowerCAmelCase				:			str    			=      word
          return word
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Dict=None    ):
  
  
  
  
  
  
          if self.add_bos_token:
              lowerCAmelCase				:			List[str]    			=      [self.bos_token_id]
          else:
              lowerCAmelCase				:			int    			=      []
  
          lowerCAmelCase				:			List[str]    			=      bos_token_ids + token_ids_a
  
          if token_ids_a is None:
              return output
  
          return output + bos_token_ids + token_ids_a
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    List[int]    ,				UpperCAmelCase_			:    Optional[List[int]] = None    ,				UpperCAmelCase_			:    bool = False    ):
  
  
  
  
  
  
          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=lowerCAmelCase_    ,				token_ids_a=lowerCAmelCase_    ,				already_has_special_tokens=lowerCAmelCase_    )
  
          if not self.add_bos_token:
              return super().get_special_tokens_mask(
                  token_ids_a=lowerCAmelCase_    ,				token_ids_a=lowerCAmelCase_    ,				already_has_special_tokens=lowerCAmelCase_    )
  
          if token_ids_a is None:
              return [1] + ([0] * len(lowerCAmelCase_    ))
          return [1] + ([0] * len(lowerCAmelCase_    )) + [1] + ([0] * len(lowerCAmelCase_    ))
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Optional[int]    ):
          lowerCAmelCase				:			Tuple    			=      []
          for token in re.findall(self.pat    ,				lowerCAmelCase_    ):
              lowerCAmelCase				:			Tuple    			=      ''.join(
                  self.byte_encoder[b] for b in token.encode('utf-8'    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase_    ).split(' '    )    )
          return bpe_tokens
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Tuple    ):
          return self.encoder.get(lowerCAmelCase_    ,				self.encoder.get(self.unk_token    )    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ,				UpperCAmelCase_			:    Optional[Any]    ):
          return self.decoder.get(lowerCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Dict    ):
          lowerCAmelCase				:			str    			=      ''.join(lowerCAmelCase_    )
          lowerCAmelCase				:			Any    			=      bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'    ,				errors=self.errors    )
          return text
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Optional[str] = None    ):
  
  
  
  
  
  
          if not os.path.isdir(lowerCAmelCase_    ):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
              return
          lowerCAmelCase				:			List[Any]    			=      os.path.join(
              lowerCAmelCase_    ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
          lowerCAmelCase				:			Optional[int]    			=      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'    )
  
          lowerCAmelCase				:			List[Any]    			=      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 UpperCAmelCase_    : 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!'    )
                      lowerCAmelCase				:			Optional[int]    			=      token_index
                  writer.write(' '.join(lowerCAmelCase_    ) + '\n'    )
                  index += 1
  
          return vocab_file, merge_file
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    str=False    ,				**UpperCAmelCase_			:    Optional[int]    ):
          lowerCAmelCase				:			Union[str, Any]    			=      kwargs.pop('add_prefix_space'    ,				self.add_prefix_space    )
          if is_split_into_words or add_prefix_space:
              lowerCAmelCase				:			Optional[Any]    			=      ' ' + text
          return (text, kwargs)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    "Conversation"    ):
          lowerCAmelCase				:			Any    			=      []
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowerCAmelCase_    ,				add_special_tokens=lowerCAmelCase_    ) + [self.eos_token_id]    )
          if len(lowerCAmelCase_    ) > self.model_max_length:
              lowerCAmelCase				:			Dict    			=      input_ids[-self.model_max_length :]
          return input_ids
  
  
  
  
  
  
style_context: 
  
  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,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Optional[Any]    			=      states.reshape(1,      -1,      model.config.state_dim				)
      lowerCAmelCase				:			List[Any]    			=      actions.reshape(1,      -1,      model.config.act_dim				)
      lowerCAmelCase				:			List[Any]    			=      returns_to_go.reshape(1,      -1,      1				)
      lowerCAmelCase				:			Dict    			=      timesteps.reshape(1,      -1				)
  
      if model.config.max_length is not None:
          lowerCAmelCase				:			Optional[Any]    			=      states[:, -model.config.max_length :]
          lowerCAmelCase				:			Union[str, Any]    			=      actions[:, -model.config.max_length :]
          lowerCAmelCase				:			Dict    			=      returns_to_go[:, -model.config.max_length :]
          lowerCAmelCase				:			List[str]    			=      timesteps[:, -model.config.max_length :]
  
          # pad all tokens to sequence length
          lowerCAmelCase				:			List[str]    			=      torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]				), torch.ones(states.shape[1]				)]				)
          lowerCAmelCase				:			List[Any]    			=      attention_mask.to(dtype=torch.long,      device=states.device				).reshape(1,      -1				)
          lowerCAmelCase				:			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				)
          lowerCAmelCase				:			int    			=      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				)
          lowerCAmelCase				:			Union[str, 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				)
          lowerCAmelCase				:			Tuple    			=      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:
          lowerCAmelCase				:			Optional[Any]    			=      None
  
      lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      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
  
  __A   :     Dict      =			gym.make('''Hopper-v3''')
  __A   :     Any      =			env.observation_space.shape[0]
  __A   :     Optional[Any]      =			env.action_space.shape[0]
  __A   :     Any      =			1000
  __A   :     Tuple      =			'''cuda'''
  __A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
  __A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __A   :     str      =			np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  __A   :     Dict      =			np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  __A   :     int      =			torch.from_numpy(state_mean).to(device=device)
  __A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __A   :     List[Any]      =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
  __A   :     Any      =			model.to(device)
  model.eval()
  
  for ep in range(10):
   __A     ,      __A   :     Optional[int]      =			0, 0
   __A   :     List[str]      =			env.reset()
   __A   :     Optional[int]      =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   __A   :     str      =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   __A   :     Optional[int]      =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   __A   :     Any      =			torch.zeros(0, device=device, dtype=torch.floataa)
  
   __A   :     List[Any]      =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    __A   :     Union[str, Any]      =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    __A   :     List[Any]      =			torch.cat([rewards, torch.zeros(1, device=device)])
  
    __A   :     int      =			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   :     List[str]      =			action
    __A   :     List[str]      =			action.detach().cpu().numpy()
  
    __A     ,      __A     ,      __A     ,      __A   :     List[str]      =			env.step(action)
  
    __A   :     Union[str, Any]      =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    __A   :     Any      =			torch.cat([states, cur_state], dim=0)
    __A   :     Dict      =			reward
  
    __A   :     str      =			target_return[0, -1] - (reward / scale)
    __A   :     Dict      =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    __A   :     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
  
  
  
  
  
  