





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 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""laion/clap-htsat-unfused"""
           SCREAMING_SNAKE_CASE	=		tempfile.mkdtemp()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,**lowerCamelCase__		:					Optional[Any] )  ->	List[Any]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,**lowerCamelCase__		:					str )  ->	Optional[int]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Optional[int]:




           '''simple docstring'''


           shutil.rmtree(self.tmpdirname )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_tokenizer()
           SCREAMING_SNAKE_CASE	=		self.get_feature_extractor()

           SCREAMING_SNAKE_CASE	=		ClapProcessor(tokenizer=lowerCamelCase__		,feature_extractor=lowerCamelCase__ )

           processor.save_pretrained(self.tmpdirname )
           SCREAMING_SNAKE_CASE	=		ClapProcessor.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        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		ClapProcessor(tokenizer=self.get_tokenizer()		,feature_extractor=self.get_feature_extractor() )
           processor.save_pretrained(self.tmpdirname )

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

           SCREAMING_SNAKE_CASE	=		ClapProcessor.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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_feature_extractor()
           SCREAMING_SNAKE_CASE	=		self.get_tokenizer()

           SCREAMING_SNAKE_CASE	=		ClapProcessor(tokenizer=lowerCamelCase__		,feature_extractor=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		floats_list((3, 1000) )

           SCREAMING_SNAKE_CASE	=		feature_extractor(lowerCamelCase__		,return_tensors="""np""" )
           SCREAMING_SNAKE_CASE	=		processor(audios=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        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_feature_extractor()
           SCREAMING_SNAKE_CASE	=		self.get_tokenizer()

           SCREAMING_SNAKE_CASE	=		ClapProcessor(tokenizer=lowerCamelCase__		,feature_extractor=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		"""This is a test string"""

           SCREAMING_SNAKE_CASE	=		processor(text=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tokenizer(lowerCamelCase__ )

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_feature_extractor()
           SCREAMING_SNAKE_CASE	=		self.get_tokenizer()

           SCREAMING_SNAKE_CASE	=		ClapProcessor(tokenizer=lowerCamelCase__		,feature_extractor=lowerCamelCase__ )

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

           SCREAMING_SNAKE_CASE	=		processor.batch_decode(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.batch_decode(lowerCamelCase__ )

           self.assertListEqual(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_feature_extractor()
           SCREAMING_SNAKE_CASE	=		self.get_tokenizer()

           SCREAMING_SNAKE_CASE	=		ClapProcessor(tokenizer=lowerCamelCase__		,feature_extractor=lowerCamelCase__ )

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









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      def get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) -> str:
            SCREAMING_SNAKE_CASE	=		[]
            SCREAMING_SNAKE_CASE	=		min(len(_stra			)				,		len(_stra			)			) // 2
            for i, l in enumerate(_stra			):
                  SCREAMING_SNAKE_CASE	=		int(max(0				,		i - limit			)			)
                  SCREAMING_SNAKE_CASE	=		int(min(i + limit + 1				,		len(_stra			)			)			)
                  if l in _stra[left:right]:
                        matched.append(_SCREAMING_SNAKE_CASE			)
                        SCREAMING_SNAKE_CASE	=		F"""{_stra[0:_stra.index(_SCREAMING_SNAKE_CASE			)]} {_stra[_stra.index(_SCREAMING_SNAKE_CASE			) + 1:]}"""

            return "".join(_SCREAMING_SNAKE_CASE			)

      # matching characters
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)

      # transposition
      SCREAMING_SNAKE_CASE	=		(
          len([(ca, ca) for ca, ca in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) if ca != ca]			) // 2
      )

      if not match_count:
            SCREAMING_SNAKE_CASE	=		0.0
      else:
            SCREAMING_SNAKE_CASE	=		(
                1
                / 3
                * (
                    match_count / len(_SCREAMING_SNAKE_CASE			)
                    + match_count / len(_SCREAMING_SNAKE_CASE			)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE	=		0
      for ca, ca in zip(stra[:4]				,		stra[:4]			):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler("""hello""", """world"""))


