






'''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 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




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

   if model.config.max_length is not None:
      lowercase_		:      Dict          =       states[:, -model.config.max_length :]
      lowercase_		:      Optional[int]          =       actions[:, -model.config.max_length :]
      lowercase_		:      str          =       returns_to_go[:, -model.config.max_length :]
      lowercase_		:      str          =       timesteps[:, -model.config.max_length :]

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

   lowercase_      ,						lowercase_      ,						lowercase_		:      Optional[Any]          =       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

UpperCamelCase__	=     gym.make('Hopper-v3')
UpperCamelCase__	=     env.observation_space.shape[0]
UpperCamelCase__	=     env.action_space.shape[0]
UpperCamelCase__	=     1000
UpperCamelCase__	=     'cuda'
UpperCamelCase__	=     10_00.0  # normalization for rewards/returns
UpperCamelCase__	=     3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase__	=     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,
    ]
)
UpperCamelCase__	=     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,
    ]
)
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












'''simple docstring'''
import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class     _UpperCAmelCase  (						snake_case			,   unittest.TestCase     ):
    __lowerCamelCase:				Optional[int]						    =      RealmTokenizer
    __lowerCamelCase:				Optional[Any]						    =      RealmTokenizerFast
    __lowerCamelCase:				Optional[int]						    =      True
    __lowerCamelCase:				str						    =      True
    __lowerCamelCase:				List[str]						    =      filter_non_english




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       super().setUp()

       lowercase_		:      Optional[int]          =       [
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "[PAD]",
           "[MASK]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       lowercase_		:      List[str]          =       os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["vocab_file"]     )
       with open(self.vocab_file					,       "w"					,       encoding="utf-8"     ) as vocab_writer:
          vocab_writer.write("".join([x + "\n" for x in vocab_tokens]     )     )




    def 			lowerCAmelCase__			(     self					:    int					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       "UNwant\u00E9d,running"
       lowercase_		:      Dict          =       "unwanted, running"
       return input_text, output_text




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      str          =       self.tokenizer_class(self.vocab_file     )

       lowercase_		:      Any          =       tokenizer.tokenize("UNwant\u00E9d,running"     )
       self.assertListEqual(a					,       ["un", "##want", "##ed", ",", "runn", "##ing"]     )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a     )					,       [9, 6, 7, 1_2, 1_0, 1_1]     )




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       if not self.test_rust_tokenizer:
          return

       lowercase_		:      Optional[Any]          =       self.get_tokenizer()
       lowercase_		:      Any          =       self.get_rust_tokenizer()

       lowercase_		:      Tuple          =       "UNwant\u00E9d,running"

       lowercase_		:      List[str]          =       tokenizer.tokenize(a     )
       lowercase_		:      Union[str, Any]          =       rust_tokenizer.tokenize(a     )
       self.assertListEqual(a					,       a     )

       lowercase_		:      Dict          =       tokenizer.encode(a					,       add_special_tokens=a     )
       lowercase_		:      Dict          =       rust_tokenizer.encode(a					,       add_special_tokens=a     )
       self.assertListEqual(a					,       a     )

       lowercase_		:      List[Any]          =       self.get_rust_tokenizer()
       lowercase_		:      Any          =       tokenizer.encode(a     )
       lowercase_		:      Optional[Any]          =       rust_tokenizer.encode(a     )
       self.assertListEqual(a					,       a     )

       # With lower casing
       lowercase_		:      Union[str, Any]          =       self.get_tokenizer(do_lower_case=a     )
       lowercase_		:      Optional[Any]          =       self.get_rust_tokenizer(do_lower_case=a     )

       lowercase_		:      int          =       "UNwant\u00E9d,running"

       lowercase_		:      List[str]          =       tokenizer.tokenize(a     )
       lowercase_		:      Dict          =       rust_tokenizer.tokenize(a     )
       self.assertListEqual(a					,       a     )

       lowercase_		:      Optional[Any]          =       tokenizer.encode(a					,       add_special_tokens=a     )
       lowercase_		:      Tuple          =       rust_tokenizer.encode(a					,       add_special_tokens=a     )
       self.assertListEqual(a					,       a     )

       lowercase_		:      Dict          =       self.get_rust_tokenizer()
       lowercase_		:      Tuple          =       tokenizer.encode(a     )
       lowercase_		:      int          =       rust_tokenizer.encode(a     )
       self.assertListEqual(a					,       a     )




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Any          =       BasicTokenizer()

       self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"     )					,       ["ah", "\u535A", "\u63A8", "zz"]     )




    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       lowercase_		:      str          =       BasicTokenizer(do_lower_case=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "     )					,       ["hello", "!", "how", "are", "you", "?"]     )
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     )					,       ["hello"]     )




    def 			lowerCAmelCase__			(     self					:    Any     ):






       '''simple docstring'''






       lowercase_		:      Optional[int]          =       BasicTokenizer(do_lower_case=a					,       strip_accents=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["hällo", "!", "how", "are", "you", "?"]     )
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     )					,       ["h\u00E9llo"]     )




    def 			lowerCAmelCase__			(     self					:    Any     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       BasicTokenizer(do_lower_case=a					,       strip_accents=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["hallo", "!", "how", "are", "you", "?"]     )
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     )					,       ["hello"]     )




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       BasicTokenizer(do_lower_case=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["hallo", "!", "how", "are", "you", "?"]     )
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     )					,       ["hello"]     )




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      Any          =       BasicTokenizer(do_lower_case=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "     )					,       ["HeLLo", "!", "how", "Are", "yoU", "?"]     )




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       BasicTokenizer(do_lower_case=a					,       strip_accents=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["HäLLo", "!", "how", "Are", "yoU", "?"]     )




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       lowercase_		:      str          =       BasicTokenizer(do_lower_case=a					,       strip_accents=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["HaLLo", "!", "how", "Are", "yoU", "?"]     )




    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       BasicTokenizer(do_lower_case=a					,       never_split=["[UNK]"]     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"     )					,       ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]     )




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       lowercase_		:      int          =       ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

       lowercase_		:      Tuple          =       {}
       for i, token in enumerate(a     ):
          lowercase_		:      Union[str, Any]          =       i
       lowercase_		:      List[str]          =       WordpieceTokenizer(vocab=a					,       unk_token="[UNK]"     )

       self.assertListEqual(tokenizer.tokenize(""     )					,       []     )

       self.assertListEqual(tokenizer.tokenize("unwanted running"     )					,       ["un", "##want", "##ed", "runn", "##ing"]     )

       self.assertListEqual(tokenizer.tokenize("unwantedX running"     )					,       ["[UNK]", "runn", "##ing"]     )




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       self.assertTrue(_is_whitespace(" "     )     )
       self.assertTrue(_is_whitespace("\t"     )     )
       self.assertTrue(_is_whitespace("\r"     )     )
       self.assertTrue(_is_whitespace("\n"     )     )
       self.assertTrue(_is_whitespace("\u00A0"     )     )

       self.assertFalse(_is_whitespace("A"     )     )
       self.assertFalse(_is_whitespace("-"     )     )




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       self.assertTrue(_is_control("\u0005"     )     )

       self.assertFalse(_is_control("A"     )     )
       self.assertFalse(_is_control(" "     )     )
       self.assertFalse(_is_control("\t"     )     )
       self.assertFalse(_is_control("\r"     )     )




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       self.assertTrue(_is_punctuation("-"     )     )
       self.assertTrue(_is_punctuation("$"     )     )
       self.assertTrue(_is_punctuation("`"     )     )
       self.assertTrue(_is_punctuation("."     )     )

       self.assertFalse(_is_punctuation("A"     )     )
       self.assertFalse(_is_punctuation(" "     )     )




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       self.get_tokenizer()

       # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
       self.assertListEqual([tokenizer.tokenize(a     ) for t in ["Test", "\xad", "test"]]					,       [["[UNK]"], [], ["[UNK]"]]     )

       if self.test_rust_tokenizer:
          lowercase_		:      Dict          =       self.get_rust_tokenizer()
          self.assertListEqual(
              [rust_tokenizer.tokenize(a     ) for t in ["Test", "\xad", "test"]]					,       [["[UNK]"], [], ["[UNK]"]]     )




    @slow
    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       self.tokenizer_class.from_pretrained("bert-base-uncased"     )

       lowercase_		:      List[str]          =       tokenizer.encode("sequence builders"					,       add_special_tokens=a     )
       lowercase_		:      Optional[Any]          =       tokenizer.encode("multi-sequence build"					,       add_special_tokens=a     )

       lowercase_		:      Optional[Any]          =       tokenizer.build_inputs_with_special_tokens(a     )
       lowercase_		:      List[str]          =       tokenizer.build_inputs_with_special_tokens(a					,       a     )

       assert encoded_sentence == [1_0_1] + text + [1_0_2]
       assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
             lowercase_		:      Any          =       self.rust_tokenizer_class.from_pretrained(a					,       **a     )

             lowercase_		:      Dict          =       f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
             lowercase_		:      Union[str, Any]          =       tokenizer_r.encode_plus(
                 a					,       return_attention_mask=a					,       return_token_type_ids=a					,       return_offsets_mapping=a					,       add_special_tokens=a					,       )

             lowercase_		:      Optional[Any]          =       tokenizer_r.do_lower_case if hasattr(a					,       "do_lower_case"     ) else False
             lowercase_		:      int          =       (
                 [
                     ((0, 0), tokenizer_r.cls_token),
                     ((0, 1), "A"),
                     ((1, 2), ","),
                     ((3, 5), "na"),
                     ((5, 6), "##ï"),
                     ((6, 8), "##ve"),
                     ((9, 1_5), tokenizer_r.mask_token),
                     ((1_6, 2_1), "Allen"),
                     ((2_1, 2_3), "##NL"),
                     ((2_3, 2_4), "##P"),
                     ((2_5, 3_3), "sentence"),
                     ((3_3, 3_4), "."),
                     ((0, 0), tokenizer_r.sep_token),
                 ]
                 if not do_lower_case
                 else [
                     ((0, 0), tokenizer_r.cls_token),
                     ((0, 1), "a"),
                     ((1, 2), ","),
                     ((3, 8), "naive"),
                     ((9, 1_5), tokenizer_r.mask_token),
                     ((1_6, 2_1), "allen"),
                     ((2_1, 2_3), "##nl"),
                     ((2_3, 2_4), "##p"),
                     ((2_5, 3_3), "sentence"),
                     ((3_3, 3_4), "."),
                     ((0, 0), tokenizer_r.sep_token),
                 ]
             )

             self.assertEqual(
                 [e[1] for e in expected_results]					,       tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]     )     )
             self.assertEqual([e[0] for e in expected_results]					,       tokens["offset_mapping"]     )





    @slow
    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
             lowercase_		:      Optional[Any]          =       self.rust_tokenizer_class.from_pretrained(a					,       **a     )
             lowercase_		:      List[str]          =       self.tokenizer_class.from_pretrained(a					,       **a     )
             lowercase_		:      List[str]          =       [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]

             lowercase_		:      int          =       tokenizer_r.batch_encode_candidates(a					,       max_length=1_0					,       return_tensors="np"     )
             lowercase_		:      List[str]          =       tokenizer_p.batch_encode_candidates(a					,       max_length=1_0					,       return_tensors="np"     )

             lowercase_		:      Any          =       (2, 2, 1_0)

             self.assertEqual(encoded_sentence_r["input_ids"].shape					,       a     )
             self.assertEqual(encoded_sentence_r["attention_mask"].shape					,       a     )
             self.assertEqual(encoded_sentence_r["token_type_ids"].shape					,       a     )

             self.assertEqual(encoded_sentence_p["input_ids"].shape					,       a     )
             self.assertEqual(encoded_sentence_p["attention_mask"].shape					,       a     )
             self.assertEqual(encoded_sentence_p["token_type_ids"].shape					,       a     )




