

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 _UpperCamelCase     =    None


_UpperCamelCase     =    logging.get_logger(__name__)

_UpperCamelCase     =    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_UpperCamelCase     =    {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

_UpperCamelCase     =    {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
}

# fmt: off
_UpperCamelCase     =    ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']



class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


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

 __SCREAMING_SNAKE_CASE      							=       []
 __SCREAMING_SNAKE_CASE      							=       []




 def __init__(self , __a=None , __a=None , __a=None , __a=None , __a="</s>" , __a="</s>" , __a="<s>" , __a="<unk>" , __a="<pad>" , __a="<mask>" , **__a , )				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       AddedToken(__a , lstrip=__a , rstrip=__a				) if isinstance(__a , __a				) else mask_token

  UpperCAmelCase__           =       kwargs.get('additional_special_tokens' , []				)
  kwargs["additional_special_tokens"] += [
      code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
  ]

  super().__init__(
      __a , src_lang=__a , tgt_lang=__a , tokenizer_file=__a , eos_token=__a , sep_token=__a , cls_token=__a , unk_token=__a , pad_token=__a , mask_token=__a , **__a , )

  UpperCAmelCase__           =       vocab_file
  UpperCAmelCase__           =       False if not self.vocab_file else True

  UpperCAmelCase__           =       {
      lang_code: self.convert_tokens_to_ids(__a				) for lang_code in FAIRSEQ_LANGUAGE_CODES
  }

  UpperCAmelCase__           =       src_lang if src_lang is not None else 'en_XX'
  UpperCAmelCase__           =       tgt_lang
  UpperCAmelCase__           =       self.lang_code_to_id[self._src_lang]
  self.set_src_lang_special_tokens(self._src_lang				)




 @property
 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  return self._src_lang




 @src_lang.setter
 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       new_src_lang
  self.set_src_lang_special_tokens(self._src_lang				)




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






  """simple docstring"""





  if token_ids_a is None:
   return self.prefix_tokens + token_ids_a + self.suffix_tokens
  # We don't expect to process pairs, but leave the pair logic for API consistency
  return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




 def 						UpperCamelCase__							(self , __a , __a = "en_XX" , __a = None , __a = "ro_RO" , **__a , )				->      BatchEncoding:






  """simple docstring"""





  UpperCAmelCase__           =       src_lang
  UpperCAmelCase__           =       tgt_lang
  return super().prepare_seqaseq_batch(__a , __a , **__a				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return self.set_src_lang_special_tokens(self.src_lang				)




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






  """simple docstring"""





  return self.set_tgt_lang_special_tokens(self.tgt_lang				)




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       self.convert_tokens_to_ids(__a				)
  UpperCAmelCase__           =       [self.cur_lang_code_id]
  UpperCAmelCase__           =       [self.eos_token_id]

  UpperCAmelCase__           =       self.convert_ids_to_tokens(self.prefix_tokens				)
  UpperCAmelCase__           =       self.convert_ids_to_tokens(self.suffix_tokens				)

  UpperCAmelCase__           =       processors.TemplateProcessing(
      single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens				)				) , )




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       self.convert_tokens_to_ids(__a				)
  UpperCAmelCase__           =       [self.cur_lang_code_id]
  UpperCAmelCase__           =       [self.eos_token_id]

  UpperCAmelCase__           =       self.convert_ids_to_tokens(self.prefix_tokens				)
  UpperCAmelCase__           =       self.convert_ids_to_tokens(self.suffix_tokens				)

  UpperCAmelCase__           =       processors.TemplateProcessing(
      single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens				)				) , )




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , **__a				)				->      int:






  """simple docstring"""





  if src_lang is None or tgt_lang is None:
   raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
  UpperCAmelCase__           =       src_lang
  UpperCAmelCase__           =       self(__a , add_special_tokens=__a , return_tensors=__a , **__a				)
  UpperCAmelCase__           =       self.convert_tokens_to_ids(__a				)
  UpperCAmelCase__           =       tgt_lang_id
  return inputs




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






  """simple docstring"""





  if not self.can_save_slow_tokenizer:
   raise ValueError(
       'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
       'tokenizer.'				)

  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']				)

  if os.path.abspath(self.vocab_file				) != os.path.abspath(__a				):
   copyfile(self.vocab_file , __a				)

  return (out_vocab_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 		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



