'''simple docstring'''
import shutil
import tempfile
import unittest

import pytest

from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio

from .test_feature_extraction_whisper import floats_list


if is_speech_available():
  from transformers import WhisperFeatureExtractor, WhisperProcessor


a=	5_0_3_5_8
a=	5_0_3_6_2






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







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	'openai/whisper-small.en'
         __UpperCamelCase						:						Optional[Any]		     =	tempfile.mkdtemp()







     def lowerCAmelCase  (		self  ,      **_lowerCamelCase     ):
         return WhisperTokenizer.from_pretrained(self.checkpoint  ,      **_lowerCamelCase     )







     def lowerCAmelCase  (		self  ,      **_lowerCamelCase     ):
         return WhisperFeatureExtractor.from_pretrained(self.checkpoint  ,      **_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         shutil.rmtree(self.tmpdirname     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.get_tokenizer()
         __UpperCamelCase						:						Tuple		     =	self.get_feature_extractor()

         __UpperCamelCase						:						List[str]		     =	WhisperProcessor(tokenizer=_lowerCamelCase  ,      feature_extractor=_lowerCamelCase     )

         processor.save_pretrained(self.tmpdirname     )
         __UpperCamelCase						:						Any		     =	WhisperProcessor.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 lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	WhisperProcessor(tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()     )
         processor.save_pretrained(self.tmpdirname     )

         __UpperCamelCase						:						Optional[int]		     =	self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'     )
         __UpperCamelCase						:						Tuple		     =	self.get_feature_extractor(do_normalize=_lowerCamelCase  ,      padding_value=1.0     )

         __UpperCamelCase						:						Optional[int]		     =	WhisperProcessor.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 lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	self.get_feature_extractor()
         __UpperCamelCase						:						Optional[Any]		     =	self.get_tokenizer()

         __UpperCamelCase						:						Optional[Any]		     =	WhisperProcessor(tokenizer=_lowerCamelCase  ,      feature_extractor=_lowerCamelCase     )

         __UpperCamelCase						:						Optional[Any]		     =	floats_list((3, 1_0_0_0)     )

         __UpperCamelCase						:						Any		     =	feature_extractor(_lowerCamelCase  ,      return_tensors='np'     )
         __UpperCamelCase						:						List[str]		     =	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 lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	self.get_feature_extractor()
         __UpperCamelCase						:						Any		     =	self.get_tokenizer()

         __UpperCamelCase						:						Union[str, Any]		     =	WhisperProcessor(tokenizer=_lowerCamelCase  ,      feature_extractor=_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	'This is a test string'

         __UpperCamelCase						:						Dict		     =	processor(text=_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	tokenizer(_lowerCamelCase     )

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	self.get_feature_extractor()
         __UpperCamelCase						:						Optional[Any]		     =	self.get_tokenizer()

         __UpperCamelCase						:						Union[str, Any]		     =	WhisperProcessor(tokenizer=_lowerCamelCase  ,      feature_extractor=_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __UpperCamelCase						:						Any		     =	processor.batch_decode(_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	tokenizer.batch_decode(_lowerCamelCase     )

         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	self.get_feature_extractor()
         __UpperCamelCase						:						Dict		     =	self.get_tokenizer()

         __UpperCamelCase						:						List[str]		     =	WhisperProcessor(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'  ,      )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						str		     =	self.get_feature_extractor()
         __UpperCamelCase						:						Dict		     =	self.get_tokenizer()

         __UpperCamelCase						:						Optional[Any]		     =	WhisperProcessor(tokenizer=_lowerCamelCase  ,      feature_extractor=_lowerCamelCase     )
         __UpperCamelCase						:						int		     =	processor.get_decoder_prompt_ids(task='transcribe'  ,      no_timestamps=_lowerCamelCase     )

         self.assertIsInstance(_lowerCamelCase  ,      _lowerCamelCase     )
         for ids in forced_decoder_ids:
             self.assertIsInstance(_lowerCamelCase  ,      (list, tuple)     )

         __UpperCamelCase						:						Tuple		     =	[TRANSCRIBE, NOTIMESTAMPS]
         self.assertListEqual([ids[-1] for ids in forced_decoder_ids]  ,      _lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	WhisperProcessor(tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()     )
         __UpperCamelCase						:						Tuple		     =	processor.get_prompt_ids('Mr. Quilter'     )
         __UpperCamelCase						:						List[str]		     =	processor.tokenizer.decode(_lowerCamelCase     )

         self.assertListEqual(prompt_ids.tolist()  ,      [5_0_3_6_0, 1_7_7_0, 1_3, 2_2_6_4, 3_4_6, 3_5_3]     )
         self.assertEqual(_lowerCamelCase  ,      '<|startofprev|> Mr. Quilter'     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	WhisperProcessor(tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()     )
         __UpperCamelCase						:						Dict		     =	processor.get_prompt_ids(''     )
         __UpperCamelCase						:						Tuple		     =	processor.tokenizer.decode(_lowerCamelCase     )

         self.assertListEqual(prompt_ids.tolist()  ,      [5_0_3_6_0, 2_2_0]     )
         self.assertEqual(_lowerCamelCase  ,      '<|startofprev|> '     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	WhisperProcessor(tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()     )

         def _test_prompt_error_raised_helper(_lowerCamelCase  ,      _lowerCamelCase     ):
             with pytest.raises(_lowerCamelCase     ) as excinfo:
                 processor.get_prompt_ids(_lowerCamelCase     )
             __UpperCamelCase						:						List[Any]		     =	f"""Encountered text in the prompt corresponding to disallowed special token: {special_token}."""
             self.assertEqual(_lowerCamelCase  ,      str(excinfo.value     )     )

         _test_prompt_error_raised_helper('<|startofprev|> test'  ,      '<|startofprev|>'     )
         _test_prompt_error_raised_helper('test <|notimestamps|>'  ,      '<|notimestamps|>'     )
         _test_prompt_error_raised_helper('test <|zh|> test <|transcribe|>'  ,      '<|zh|>'     )




'''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 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Dict					,  _a						:						Optional[int]					,  _a						:						Dict					,  _a						:						int					,  _a						:						List[str]      ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	states.reshape(1					,  -1					,  model.config.state_dim      )
    __UpperCamelCase						:						Tuple		     =	actions.reshape(1					,  -1					,  model.config.act_dim      )
    __UpperCamelCase						:						Optional[Any]		     =	returns_to_go.reshape(1					,  -1					,  1      )
    __UpperCamelCase						:						Optional[int]		     =	timesteps.reshape(1					,  -1      )

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

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

    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	model(
        states=_a					,  actions=_a					,  rewards=_a					,  returns_to_go=_a					,  timesteps=_a					,  attention_mask=_a					,  return_dict=_a					,  )

    return action_preds[0, -1]


# build the environment

a=	gym.make('''Hopper-v3''')
a=	env.observation_space.shape[0]
a=	env.action_space.shape[0]
a=	1_0_0_0
a=	'''cuda'''
a=	1_000.0  # normalization for rewards/returns
a=	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a=	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,
    ]
)
a=	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,
    ]
)
a=	torch.from_numpy(state_mean).to(device=device)
a=	torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
  a,      a=	0, 0
  a=	env.reset()
  a=	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  a=	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  a=	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  a=	torch.zeros(0, device=device, dtype=torch.floataa)

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

    a=	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),
    )
    a=	action
    a=	action.detach().cpu().numpy()

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

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

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



