


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_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase							):
   # we don't care about the past rewards in this model

   UpperCAmelCase_				:   List[Any]							= states.reshape(1      ,							-1      ,							model.config.state_dim							)
   UpperCAmelCase_				:   Optional[Any]							= actions.reshape(1      ,							-1      ,							model.config.act_dim							)
   UpperCAmelCase_				:   int							= returns_to_go.reshape(1      ,							-1      ,							1							)
   UpperCAmelCase_				:   int							= timesteps.reshape(1      ,							-1							)

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

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

   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_				:   Optional[Any]							= model(
       states=__lowercase      ,							actions=__lowercase      ,							rewards=__lowercase      ,							returns_to_go=__lowercase      ,							timesteps=__lowercase      ,							attention_mask=__lowercase      ,							return_dict=__lowercase      ,							)

   return action_preds[0, -1]


# build the environment

__UpperCamelCase					:	Optional[int]          =				gym.make('Hopper-v3')
__UpperCamelCase					:	List[str]          =				env.observation_space.shape[0]
__UpperCamelCase					:	Union[str, Any]          =				env.action_space.shape[0]
__UpperCamelCase					:	Dict          =				1000
__UpperCamelCase					:	Tuple          =				'cuda'
__UpperCamelCase					:	Union[str, Any]          =				1_000.0  # normalization for rewards/returns
__UpperCamelCase					:	Any          =				3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__UpperCamelCase					:	List[Any]          =				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,
    ]
)
__UpperCamelCase					:	Optional[Any]          =				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,
    ]
)
__UpperCamelCase					:	Optional[Any]          =				torch.from_numpy(state_mean).to(device=device)
__UpperCamelCase					:	Union[str, Any]          =				torch.from_numpy(state_std).to(device=device)

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

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

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

              __UpperCamelCase					:	Union[str, 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),
              )
              __UpperCamelCase					:	List[str]          =				action
              __UpperCamelCase					:	Any          =				action.detach().cpu().numpy()

              __UpperCamelCase       ,       __UpperCamelCase       ,       __UpperCamelCase       ,       __UpperCamelCase					:	int          =				env.step(action)

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

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

__UpperCamelCase					:	Dict          =				logging.get_logger(__name__)

__UpperCamelCase					:	List[str]          =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__UpperCamelCase					:	Optional[int]          =				{
    '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',
    },
}

__UpperCamelCase					:	Any          =				{
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}
@lru_cache()
def   snake_case_    (			):
   UpperCAmelCase_				:   Union[str, Any]							= (
       list(range(ord('''!'''							)      ,							ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)      ,							ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)      ,							ord('''ÿ'''							) + 1							)							)
   )
   UpperCAmelCase_				:   Optional[int]							= bs[:]
   UpperCAmelCase_				:   Union[str, Any]							= 0
   for b in range(2**8							):
      if b not in bs:
         bs.append(__lowercase							)
         cs.append(2**8 + n							)
         n += 1
   UpperCAmelCase_				:   Dict							= [chr(__lowercase							) for n in cs]
   return dict(zip(__lowercase      ,							__lowercase							)							)
def   snake_case_    (			__lowercase							):
   UpperCAmelCase_				:   List[str]							= set()
   UpperCAmelCase_				:   Optional[Any]							= word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)							)
      UpperCAmelCase_				:   Union[str, Any]							= char
   return pairs





class 			lowerCAmelCase__(     snake_case__    ):






    '''simple docstring'''





    A_	:  Optional[int]								=			VOCAB_FILES_NAMES
    A_	:  Any								=			PRETRAINED_VOCAB_FILES_MAP
    A_	:  Dict								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    A_	:  List[str]								=			['input_ids', 'attention_mask']





    def __init__(						self					: Optional[int]							, __snake_case					: Dict							, __snake_case					: List[Any]							, __snake_case					: List[Any]="replace"							, __snake_case					: Any="<|endoftext|>"							, __snake_case					: Union[str, Any]="<|endoftext|>"							, __snake_case					: List[str]="<|endoftext|>"							, __snake_case					: Any=None							, __snake_case					: List[str]=False							, __snake_case					: Optional[int]=False							, **__snake_case					: str							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Optional[int]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else bos_token
       UpperCAmelCase_				:   List[Any]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else eos_token
       UpperCAmelCase_				:   Optional[int]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else unk_token
       UpperCAmelCase_				:   Tuple							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else pad_token
       super().__init__(
           errors=__snake_case							, unk_token=__snake_case							, bos_token=__snake_case							, eos_token=__snake_case							, pad_token=__snake_case							, add_prefix_space=__snake_case							, add_bos_token=__snake_case							, **__snake_case							, )
       UpperCAmelCase_				:   List[str]							= add_bos_token

       with open(__snake_case							, encoding='''utf-8'''						) as vocab_handle:
          UpperCAmelCase_				:   Optional[int]							= json.load(__snake_case						)
       UpperCAmelCase_				:   int							= {v: k for k, v in self.encoder.items()}
       UpperCAmelCase_				:   Union[str, Any]							= errors  # how to handle errors in decoding
       UpperCAmelCase_				:   Optional[int]							= bytes_to_unicode()
       UpperCAmelCase_				:   str							= {v: k for k, v in self.byte_encoder.items()}
       with open(__snake_case							, encoding='''utf-8'''						) as merges_handle:
          UpperCAmelCase_				:   Any							= merges_handle.read().split('''\n'''						)[1:-1]
       UpperCAmelCase_				:   List[str]							= [tuple(merge.split()						) for merge in bpe_merges]
       UpperCAmelCase_				:   Optional[int]							= dict(zip(__snake_case							, range(len(__snake_case						)						)						)						)
       UpperCAmelCase_				:   Any							= {}
       UpperCAmelCase_				:   int							= add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCAmelCase_				:   int							= re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''						)





    @property
    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       return len(self.encoder						)





    def        _lowerCamelCase			(						self					: str						):





       '''simple docstring'''
       return dict(self.encoder							, **self.added_tokens_encoder						)





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: Optional[Any]						):





       '''simple docstring'''
       if token in self.cache:
          return self.cache[token]
       UpperCAmelCase_				:   Any							= tuple(__snake_case						)
       UpperCAmelCase_				:   Any							= get_pairs(__snake_case						)

       if not pairs:
          return token

       while True:
          UpperCAmelCase_				:   Any							= min(__snake_case							, key=lambda __snake_case						: self.bpe_ranks.get(__snake_case							, float('''inf'''						)						)						)
          if bigram not in self.bpe_ranks:
             break
          UpperCAmelCase_		,   UpperCAmelCase_				:   Dict							= bigram
          UpperCAmelCase_				:   List[Any]							= []
          UpperCAmelCase_				:   Union[str, Any]							= 0
          while i < len(__snake_case						):
             try:
                UpperCAmelCase_				:   Optional[int]							= word.index(__snake_case							, __snake_case						)
             except ValueError:
                new_word.extend(word[i:]						)
                break
             else:
                new_word.extend(word[i:j]						)
                UpperCAmelCase_				:   str							= 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
          UpperCAmelCase_				:   Any							= tuple(__snake_case						)
          UpperCAmelCase_				:   Tuple							= new_word
          if len(__snake_case						) == 1:
             break
          else:
             UpperCAmelCase_				:   str							= get_pairs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= ''' '''.join(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= word
       return word





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: Any							, __snake_case					: List[str]=None						):





       '''simple docstring'''
       if self.add_bos_token:
          UpperCAmelCase_				:   List[Any]							= [self.bos_token_id]
       else:
          UpperCAmelCase_				:   Dict							= []

       UpperCAmelCase_				:   Dict							= bos_token_ids + token_ids_a

       if token_ids_a is None:
          return output

       return output + bos_token_ids + token_ids_a





    def        _lowerCamelCase			(						self					: Optional[int]							, __snake_case					: List[int]							, __snake_case					: Optional[List[int]] = None							, __snake_case					: bool = False						):





       '''simple docstring'''
       if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__snake_case							, token_ids_a=__snake_case							, already_has_special_tokens=__snake_case						)

       if not self.add_bos_token:
          return super().get_special_tokens_mask(
              token_ids_a=__snake_case							, token_ids_a=__snake_case							, already_has_special_tokens=__snake_case						)

       if token_ids_a is None:
          return [1] + ([0] * len(__snake_case						))
       return [1] + ([0] * len(__snake_case						)) + [1] + ([0] * len(__snake_case						))





    def        _lowerCamelCase			(						self					: str							, __snake_case					: Union[str, Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= []
       for token in re.findall(self.pat							, __snake_case						):
          UpperCAmelCase_				:   Any							= ''''''.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(__snake_case						).split(''' '''						)						)
       return bpe_tokens





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: Optional[int]						):





       '''simple docstring'''
       return self.encoder.get(__snake_case							, self.encoder.get(self.unk_token						)						)





    def        _lowerCamelCase			(						self					: int							, __snake_case					: str						):





       '''simple docstring'''
       return self.decoder.get(__snake_case						)





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: List[str]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= ''''''.join(__snake_case						)
       UpperCAmelCase_				:   str							= bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8'''							, errors=self.errors						)
       return text





    def        _lowerCamelCase			(						self					: str							, __snake_case					: str							, __snake_case					: Optional[str] = None						):





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

       UpperCAmelCase_				:   Union[str, Any]							= 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!'''						)
                UpperCAmelCase_				:   Dict							= token_index
             writer.write(''' '''.join(__snake_case						) + '''\n'''						)
             index += 1

       return vocab_file, merge_file





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: str							, __snake_case					: List[Any]=False							, **__snake_case					: Dict						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= kwargs.pop('''add_prefix_space'''							, self.add_prefix_space						)
       if is_split_into_words or add_prefix_space:
          UpperCAmelCase_				:   str							= ''' ''' + text
       return (text, kwargs)





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: "Conversation"						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= []
       for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(__snake_case							, add_special_tokens=__snake_case						) + [self.eos_token_id]						)
       if len(__snake_case						) > self.model_max_length:
          UpperCAmelCase_				:   str							= input_ids[-self.model_max_length :]
       return input_ids