

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     =    logging.get_logger(__name__)

_UpperCamelCase     =    {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

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







class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE      							=       PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE      							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE      							=       ["""input_ids""", """attention_mask"""]




 def __init__(self , __a , __a , __a="replace" , __a="<|endoftext|>" , __a="<|endoftext|>" , __a="<|endoftext|>" , __a=None , __a=False , __a=False , **__a , )				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       AddedToken(__a , lstrip=__a , rstrip=__a				) if isinstance(__a , __a				) else bos_token
  UpperCAmelCase__           =       AddedToken(__a , lstrip=__a , rstrip=__a				) if isinstance(__a , __a				) else eos_token
  UpperCAmelCase__           =       AddedToken(__a , lstrip=__a , rstrip=__a				) if isinstance(__a , __a				) else unk_token
  UpperCAmelCase__           =       AddedToken(__a , lstrip=__a , rstrip=__a				) if isinstance(__a , __a				) else pad_token
  super().__init__(
      errors=__a , unk_token=__a , bos_token=__a , eos_token=__a , pad_token=__a , add_prefix_space=__a , add_bos_token=__a , **__a , )
  UpperCAmelCase__           =       add_bos_token

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

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




 @property
 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  return len(self.encoder				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , __a				)				->      Dict:






  """simple docstring"""





  if token in self.cache:
   return self.cache[token]
  UpperCAmelCase__           =       tuple(__a				)
  UpperCAmelCase__           =       get_pairs(__a				)

  if not pairs:
   return token

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

    if word[i] == first and i < len(__a				) - 1 and word[i + 1] == second:
     new_word.append(first + second				)
     i += 2
    else:
     new_word.append(word[i]				)
     i += 1
   UpperCAmelCase__           =       tuple(__a				)
   UpperCAmelCase__           =       new_word
   if len(__a				) == 1:
    break
   else:
    UpperCAmelCase__           =       get_pairs(__a				)
  UpperCAmelCase__           =       ' '.join(__a				)
  UpperCAmelCase__           =       word
  return word




 def 						UpperCamelCase__							(self , __a , __a=None				)				->      Dict:






  """simple docstring"""





  if self.add_bos_token:
   UpperCAmelCase__           =       [self.bos_token_id]
  else:
   UpperCAmelCase__           =       []

  UpperCAmelCase__           =       bos_token_ids + token_ids_a

  if token_ids_a is None:
   return output

  return output + bos_token_ids + token_ids_a




 def 						UpperCamelCase__							(self , __a , __a = None , __a = False				)				->      List[int]:






  """simple docstring"""





  if already_has_special_tokens:
   return super().get_special_tokens_mask(
       token_ids_a=__a , token_ids_a=__a , already_has_special_tokens=__a				)

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

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




 def 						UpperCamelCase__							(self , __a				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       []
  for token in re.findall(self.pat , __a				):
   UpperCAmelCase__           =       ''.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(__a				).split(' '				)				)
  return bpe_tokens




 def 						UpperCamelCase__							(self , __a				)				->      int:






  """simple docstring"""





  return self.encoder.get(__a , self.encoder.get(self.unk_token				)				)




 def 						UpperCamelCase__							(self , __a				)				->      List[Any]:






  """simple docstring"""





  return self.decoder.get(__a				)




 def 						UpperCamelCase__							(self , __a				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       ''.join(__a				)
  UpperCAmelCase__           =       bytearray([self.byte_decoder[c] for c in text]				).decode('utf-8' , errors=self.errors				)
  return text




 def 						UpperCamelCase__							(self , __a , __a = None				)				->      Tuple[str]:






  """simple docstring"""





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

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

  UpperCAmelCase__           =       0
  with open(__a , '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 __a				: 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__           =       token_index
    writer.write(' '.join(__a				) + '\n'				)
    index += 1

  return vocab_file, merge_file




 def 						UpperCamelCase__							(self , __a , __a=False , **__a				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__           =       kwargs.pop('add_prefix_space' , self.add_prefix_space				)
  if is_split_into_words or add_prefix_space:
   UpperCAmelCase__           =       ' ' + text
  return (text, kwargs)




 def 						UpperCamelCase__							(self , __a				)				->      List[int]:






  """simple docstring"""





  UpperCAmelCase__           =       []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(__a , add_special_tokens=__a				) + [self.eos_token_id]				)
  if len(__a				) > self.model_max_length:
   UpperCAmelCase__           =       input_ids[-self.model_max_length :]
  return input_ids






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 		UpperCamelCase_(    snake_case__:						int						,					snake_case__:						int						,					snake_case__:						Union[str, Any]						,					snake_case__:						Any						,					snake_case__:						Any						,					snake_case__:						Union[str, Any]     ) ->				Any:
 # we don't care about the past rewards in this model

 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__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       model(
     states=snake_case__						,					actions=snake_case__						,					rewards=snake_case__						,					returns_to_go=snake_case__						,					timesteps=snake_case__						,					attention_mask=snake_case__						,					return_dict=snake_case__						,					)

 return action_preds[0, -1]


# build the environment

_UpperCamelCase     =    gym.make('''Hopper-v3''')
_UpperCamelCase     =    env.observation_space.shape[0]
_UpperCamelCase     =    env.action_space.shape[0]
_UpperCamelCase     =    1000
_UpperCamelCase     =    '''cuda'''
_UpperCamelCase     =    1_0_0_0.0  # normalization for rewards/returns
_UpperCamelCase     =    3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase     =    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,
    ]
)
_UpperCamelCase     =    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,
    ]
)
_UpperCamelCase     =    torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase     =    torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
 _UpperCamelCase ,       _UpperCamelCase     =    0, 0
 _UpperCamelCase     =    env.reset()
 _UpperCamelCase     =    torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 _UpperCamelCase     =    torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros(0, device=device, dtype=torch.floataa)

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

  _UpperCamelCase     =    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     =    action
  _UpperCamelCase     =    action.detach().cpu().numpy()

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

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

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



