


'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list






@require_torchaudio
@require_sentencepiece
class    __magic_name__				(						unittest.TestCase							):






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       ='laion/clap-htsat-unfused'
          _UpperCAmelCase  :  Union[str, Any]				       =tempfile.mkdtemp()






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          return RobertaTokenizer.from_pretrained(self.checkpoint    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    List[Any]:




          '''simple docstring'''
          return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,				**snake_case)






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          shutil.rmtree(self.tmpdirname)






       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.get_tokenizer()
          _UpperCAmelCase  :  int				       =self.get_feature_extractor()

          _UpperCAmelCase  :  Any				       =ClapProcessor(tokenizer=snake_case    ,				feature_extractor=snake_case)

          processor.save_pretrained(self.tmpdirname)
          _UpperCAmelCase  :  Union[str, Any]				       =ClapProcessor.from_pretrained(self.tmpdirname)

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

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






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =ClapProcessor(tokenizer=self.get_tokenizer()    ,				feature_extractor=self.get_feature_extractor())
          processor.save_pretrained(self.tmpdirname)

          _UpperCAmelCase  :  List[Any]				       =self.get_tokenizer(bos_token='(BOS)'    ,				eos_token='(EOS)')
          _UpperCAmelCase  :  List[str]				       =self.get_feature_extractor(do_normalize=snake_case    ,				padding_value=1.0)

          _UpperCAmelCase  :  Any				       =ClapProcessor.from_pretrained(
              self.tmpdirname    ,				bos_token='(BOS)'    ,				eos_token='(EOS)'    ,				do_normalize=snake_case    ,				padding_value=1.0)

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

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






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =self.get_feature_extractor()
          _UpperCAmelCase  :  Union[str, Any]				       =self.get_tokenizer()

          _UpperCAmelCase  :  List[str]				       =ClapProcessor(tokenizer=snake_case    ,				feature_extractor=snake_case)

          _UpperCAmelCase  :  str				       =floats_list((3, 1_0_0_0))

          _UpperCAmelCase  :  List[Any]				       =feature_extractor(snake_case    ,				return_tensors='np')
          _UpperCAmelCase  :  Optional[int]				       =processor(audios=snake_case    ,				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)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.get_feature_extractor()
          _UpperCAmelCase  :  List[Any]				       =self.get_tokenizer()

          _UpperCAmelCase  :  Any				       =ClapProcessor(tokenizer=snake_case    ,				feature_extractor=snake_case)

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

          _UpperCAmelCase  :  Any				       =processor(text=snake_case)

          _UpperCAmelCase  :  Dict				       =tokenizer(snake_case)

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






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =self.get_feature_extractor()
          _UpperCAmelCase  :  Union[str, Any]				       =self.get_tokenizer()

          _UpperCAmelCase  :  Optional[Any]				       =ClapProcessor(tokenizer=snake_case    ,				feature_extractor=snake_case)

          _UpperCAmelCase  :  Optional[Any]				       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _UpperCAmelCase  :  Dict				       =processor.batch_decode(snake_case)
          _UpperCAmelCase  :  int				       =tokenizer.batch_decode(snake_case)

          self.assertListEqual(snake_case    ,				snake_case)






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =self.get_feature_extractor()
          _UpperCAmelCase  :  Optional[Any]				       =self.get_tokenizer()

          _UpperCAmelCase  :  List[str]				       =ClapProcessor(tokenizer=snake_case    ,				feature_extractor=snake_case)

          self.assertListEqual(
              processor.model_input_names[2:]    ,				feature_extractor.model_input_names    ,				msg='`processor` and `feature_extractor` model input names do not match'    ,				)





'''simple docstring'''






import warnings
from functools import wraps
from typing import Callable





def lowerCamelCase__     (		__lowerCamelCase			:				Callable				):







   '''simple docstring'''




   @wraps(__lowerCamelCase				)
   def _inner_fn(*__lowerCamelCase			:				int			,       **__lowerCamelCase			:				Optional[Any]				):
      warnings.warn(
          (f"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")			,       __lowerCamelCase			,       )
      return fn(*__lowerCamelCase			,       **__lowerCamelCase				)

   return _inner_fn

