





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							):





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        A_    : str								=	'laion/clap-htsat-unfused'
        A_    : Dict								=	tempfile.mkdtemp()



     def   SCREAMING_SNAKE_CASE						(							self				:str	,					**snake_case				:str     ):





        '''simple docstring'''




        return RobertaTokenizer.from_pretrained(self.checkpoint	,					**_A     )



     def   SCREAMING_SNAKE_CASE						(							self				:Any	,					**snake_case				:List[Any]     ):





        '''simple docstring'''




        return ClapFeatureExtractor.from_pretrained(self.checkpoint	,					**_A     )



     def   SCREAMING_SNAKE_CASE						(							self				:Tuple     ):





        '''simple docstring'''




        shutil.rmtree(self.tmpdirname     )



     def   SCREAMING_SNAKE_CASE						(							self				:Tuple     ):





        '''simple docstring'''




        A_    : Tuple								=	self.get_tokenizer()
        A_    : Any								=	self.get_feature_extractor()

        A_    : Union[str, Any]								=	ClapProcessor(tokenizer=_A	,					feature_extractor=_A     )

        processor.save_pretrained(self.tmpdirname     )
        A_    : Any								=	ClapProcessor.from_pretrained(self.tmpdirname     )

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

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



     def   SCREAMING_SNAKE_CASE						(							self				:Any     ):





        '''simple docstring'''




        A_    : str								=	ClapProcessor(tokenizer=self.get_tokenizer()	,					feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        A_    : Optional[Any]								=	self.get_tokenizer(bos_token="(BOS)"	,					eos_token="(EOS)"     )
        A_    : List[Any]								=	self.get_feature_extractor(do_normalize=_A	,					padding_value=1.0     )

        A_    : Any								=	ClapProcessor.from_pretrained(
            self.tmpdirname	,					bos_token="(BOS)"	,					eos_token="(EOS)"	,					do_normalize=_A	,					padding_value=1.0     )

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

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



     def   SCREAMING_SNAKE_CASE						(							self				:str     ):





        '''simple docstring'''




        A_    : Tuple								=	self.get_feature_extractor()
        A_    : Optional[Any]								=	self.get_tokenizer()

        A_    : Optional[int]								=	ClapProcessor(tokenizer=_A	,					feature_extractor=_A     )

        A_    : int								=	floats_list((3, 1_000)     )

        A_    : List[Any]								=	feature_extractor(_A	,					return_tensors="np"     )
        A_    : List[str]								=	processor(audios=_A	,					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   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




        A_    : Any								=	self.get_feature_extractor()
        A_    : str								=	self.get_tokenizer()

        A_    : Dict								=	ClapProcessor(tokenizer=_A	,					feature_extractor=_A     )

        A_    : Optional[Any]								=	'This is a test string'

        A_    : Any								=	processor(text=_A     )

        A_    : Optional[int]								=	tokenizer(_A     )

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



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        A_    : List[Any]								=	self.get_feature_extractor()
        A_    : Union[str, Any]								=	self.get_tokenizer()

        A_    : str								=	ClapProcessor(tokenizer=_A	,					feature_extractor=_A     )

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

        A_    : Dict								=	processor.batch_decode(_A     )
        A_    : List[Any]								=	tokenizer.batch_decode(_A     )

        self.assertListEqual(_A	,					_A     )





     def   SCREAMING_SNAKE_CASE						(							self				:Any     ):





        '''simple docstring'''




        A_    : List[str]								=	self.get_feature_extractor()
        A_    : Any								=	self.get_tokenizer()

        A_    : Any								=	ClapProcessor(tokenizer=_A	,					feature_extractor=_A     )

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







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




