





"""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  __lowercase	(	unittest.TestCase							):







					'''simple docstring'''




					def        _lowerCamelCase						(  self   ):
									__a       :  Any					=     'laion/clap-htsat-unfused'
									__a       :  Tuple					=     tempfile.mkdtemp()







					def        _lowerCamelCase						(  self		,	**_UpperCAmelCase   ):
									return RobertaTokenizer.from_pretrained(self.checkpoint		,	**SCREAMING_SNAKE_CASE__   )







					def        _lowerCamelCase						(  self		,	**_UpperCAmelCase   ):
									return ClapFeatureExtractor.from_pretrained(self.checkpoint		,	**SCREAMING_SNAKE_CASE__   )







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







					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[int]					=     self.get_tokenizer()
									__a       :  int					=     self.get_feature_extractor()

									__a       :  Union[str, Any]					=     ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__		,	feature_extractor=SCREAMING_SNAKE_CASE__   )

									processor.save_pretrained(self.tmpdirname   )
									__a       :  Optional[Any]					=     ClapProcessor.from_pretrained(self.tmpdirname   )

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

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







					def        _lowerCamelCase						(  self   ):
									__a       :  Any					=     ClapProcessor(tokenizer=self.get_tokenizer()		,	feature_extractor=self.get_feature_extractor()   )
									processor.save_pretrained(self.tmpdirname   )

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

									__a       :  Optional[Any]					=     ClapProcessor.from_pretrained(
									    self.tmpdirname		,	bos_token='''(BOS)'''		,	eos_token='''(EOS)'''		,	do_normalize=SCREAMING_SNAKE_CASE__		,	padding_value=1.0   )

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

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







					def        _lowerCamelCase						(  self   ):
									__a       :  Union[str, Any]					=     self.get_feature_extractor()
									__a       :  Union[str, Any]					=     self.get_tokenizer()

									__a       :  Optional[Any]					=     ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__		,	feature_extractor=SCREAMING_SNAKE_CASE__   )

									__a       :  str					=     floats_list((3, 1000)   )

									__a       :  Any					=     feature_extractor(SCREAMING_SNAKE_CASE__		,	return_tensors='''np'''   )
									__a       :  List[Any]					=     processor(audios=SCREAMING_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   ):
									__a       :  Union[str, Any]					=     self.get_feature_extractor()
									__a       :  Dict					=     self.get_tokenizer()

									__a       :  Tuple					=     ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__		,	feature_extractor=SCREAMING_SNAKE_CASE__   )

									__a       :  Tuple					=     'This is a test string'

									__a       :  List[str]					=     processor(text=SCREAMING_SNAKE_CASE__   )

									__a       :  Union[str, Any]					=     tokenizer(SCREAMING_SNAKE_CASE__   )

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







					def        _lowerCamelCase						(  self   ):
									__a       :  Tuple					=     self.get_feature_extractor()
									__a       :  Any					=     self.get_tokenizer()

									__a       :  Dict					=     ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__		,	feature_extractor=SCREAMING_SNAKE_CASE__   )

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

									__a       :  List[str]					=     processor.batch_decode(SCREAMING_SNAKE_CASE__   )
									__a       :  Dict					=     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__   )

									self.assertListEqual(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Dict					=     self.get_feature_extractor()
									__a       :  Optional[Any]					=     self.get_tokenizer()

									__a       :  Optional[int]					=     ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__		,	feature_extractor=SCREAMING_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'''		,	)



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def        SCREAMING_SNAKE_CASE_						(			)	->		Any:

       """simple docstring"""

       a_					:		Optional[Any]							=  HfArgumentParser(__A				)
       a_					:		Optional[int]							=  parser.parse_args_into_dataclasses()[0]
       a_					:		List[Any]							=  TensorFlowBenchmark(args=__A				)
       try:
              a_					:		List[str]							=  parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              a_					:		Dict							=  'Arg --no_{0} is no longer used, please use --no-{0} instead.'
              a_					:		Dict							=  ' '.join(str(__A				).split(' '				)[:-1]				)
              a_					:		int							=  ''
              a_					:		int							=  eval(str(__A				).split(' '				)[-1]				)
              a_					:		Any							=  []
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]				)
                     else:
                            wrong_args.append(__A				)
              if len(__A				) > 0:
                     a_					:		str							=  full_error_msg + begin_error_msg + str(__A				)
              raise ValueError(__A				)
       benchmark.run()


if __name__ == "__main__":
   main()


