




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 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  # 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=_lowerCAmelCase   ,     actions=_lowerCAmelCase   ,     rewards=_lowerCAmelCase   ,     returns_to_go=_lowerCAmelCase   ,     timesteps=_lowerCAmelCase   ,     attention_mask=_lowerCAmelCase   ,     return_dict=_lowerCAmelCase   ,     )

  return action_preds[0, -1]


# build the environment

snake_case__ :						List[str]     =			gym.make('''Hopper-v3''')
snake_case__ :						Tuple     =			env.observation_space.shape[0]
snake_case__ :						Dict     =			env.action_space.shape[0]
snake_case__ :						List[str]     =			1_0_0_0
snake_case__ :						str     =			'''cuda'''
snake_case__ :						int     =			10_00.0  # normalization for rewards/returns
snake_case__ :						List[str]     =			3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case__ :						Optional[int]     =			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,
    ]
)
snake_case__ :						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,
    ]
)
snake_case__ :						str     =			torch.from_numpy(state_mean).to(device=device)
snake_case__ :						Any     =			torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
   snake_case__    ,      snake_case__ :						Optional[int]     =			0, 0
   snake_case__ :						int     =			env.reset()
   snake_case__ :						int     =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   snake_case__ :						List[str]     =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   snake_case__ :						Dict     =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   snake_case__ :						int     =			torch.zeros(0, device=device, dtype=torch.floataa)

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

      snake_case__ :						List[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),
      )
      snake_case__ :						List[str]     =			action
      snake_case__ :						int     =			action.detach().cpu().numpy()

      snake_case__    ,      snake_case__    ,      snake_case__    ,      snake_case__ :						Any     =			env.step(action)

      snake_case__ :						Dict     =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
      snake_case__ :						Optional[int]     =			torch.cat([states, cur_state], dim=0)
      snake_case__ :						Tuple     =			reward

      snake_case__ :						int     =			target_return[0, -1] - (reward / scale)
      snake_case__ :						Any     =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
      snake_case__ :						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 shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


snake_case__ :						Union[str, Any]     =			get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_torch
@require_torchaudio
@require_sentencepiece
class     snake_case  (	unittest.TestCase							):


   '''simple docstring'''







   def 	UpperCAmelCase       (					self     :	str			)					->str:
     '''simple docstring'''







     UpperCAmelCase__    						=   tempfile.mkdtemp()

     UpperCAmelCase__    						=   ["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
     UpperCAmelCase__    						=   dict(zip(lowerCamelCase_						,    range(len(lowerCamelCase_			)			)			)			)
     UpperCAmelCase__    						=   Path(self.tmpdirname			)
     save_json(lowerCamelCase_						,    save_dir / VOCAB_FILES_NAMES["""vocab_file"""]			)
     if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(lowerCamelCase_						,    save_dir / VOCAB_FILES_NAMES["""spm_file"""]			)

     UpperCAmelCase__    						=   SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
     tokenizer.save_pretrained(self.tmpdirname			)

     UpperCAmelCase__    						=   {
         """feature_size""": 24,
         """num_mel_bins""": 24,
         """padding_value""": 0.0,
         """sampling_rate""": 1_6000,
         """return_attention_mask""": False,
         """do_normalize""": True,
     }
     save_json(lowerCamelCase_						,    save_dir / FEATURE_EXTRACTOR_NAME			)




   def 	UpperCAmelCase       (					self     :	Union[str, Any]						,    **lowerCamelCase_     :	Tuple			)					->Any:
     '''simple docstring'''







     return SpeechaTextTokenizer.from_pretrained(self.tmpdirname						,    **lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Dict						,    **lowerCamelCase_     :	str			)					->Optional[Any]:
     '''simple docstring'''







     return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname						,    **lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	List[str]			)					->Dict:
     '''simple docstring'''







     shutil.rmtree(self.tmpdirname			)




   def 	UpperCAmelCase       (					self     :	str			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_tokenizer()
     UpperCAmelCase__    						=   self.get_feature_extractor()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

     processor.save_pretrained(self.tmpdirname			)
     UpperCAmelCase__    						=   SpeechaTextProcessor.from_pretrained(self.tmpdirname			)

     self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer.get_vocab()			)
     self.assertIsInstance(processor.tokenizer						,    lowerCamelCase_			)

     self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor						,    lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	List[str]			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   SpeechaTextProcessor(
         tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     UpperCAmelCase__    						=   self.get_tokenizer(bos_token="""(BOS)"""						,    eos_token="""(EOS)"""			)
     UpperCAmelCase__    						=   self.get_feature_extractor(do_normalize=lowerCamelCase_						,    padding_value=1.0			)

     UpperCAmelCase__    						=   SpeechaTextProcessor.from_pretrained(
         self.tmpdirname						,    bos_token="""(BOS)"""						,    eos_token="""(EOS)"""						,    do_normalize=lowerCamelCase_						,    padding_value=1.0			)

     self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()			)
     self.assertIsInstance(processor.tokenizer						,    lowerCamelCase_			)

     self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor						,    lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Union[str, Any]			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_feature_extractor()
     UpperCAmelCase__    						=   self.get_tokenizer()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

     UpperCAmelCase__    						=   floats_list((3, 1000)			)

     UpperCAmelCase__    						=   feature_extractor(lowerCamelCase_						,    return_tensors="""np"""			)
     UpperCAmelCase__    						=   processor(lowerCamelCase_						,    return_tensors="""np"""			)

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1E-2			)




   def 	UpperCAmelCase       (					self     :	str			)					->Union[str, Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_feature_extractor()
     UpperCAmelCase__    						=   self.get_tokenizer()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

     UpperCAmelCase__    						=   """This is a test string"""

     UpperCAmelCase__    						=   processor(text=lowerCamelCase_			)

     UpperCAmelCase__    						=   tokenizer(lowerCamelCase_			)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]			)




   def 	UpperCAmelCase       (					self     :	Optional[Any]			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_feature_extractor()
     UpperCAmelCase__    						=   self.get_tokenizer()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

     UpperCAmelCase__    						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase__    						=   processor.batch_decode(lowerCamelCase_			)
     UpperCAmelCase__    						=   tokenizer.batch_decode(lowerCamelCase_			)

     self.assertListEqual(lowerCamelCase_						,    lowerCamelCase_			)







   def 	UpperCAmelCase       (					self     :	Dict			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_feature_extractor()
     UpperCAmelCase__    						=   self.get_tokenizer()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

     self.assertListEqual(
         processor.model_input_names						,    feature_extractor.model_input_names						,    msg="""`processor` and `feature_extractor` model input names do not match"""						,    )




