




'''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 ):







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					'laion/clap-htsat-unfused'
     __a			=					tempfile.mkdtemp()







   def 		UpperCamelCase__			(	self      ,							**UpperCamelCase      )				->    List[str]:
     return RobertaTokenizer.from_pretrained(self.checkpoint      ,							**UpperCamelCase__      )







   def 		UpperCamelCase__			(	self      ,							**UpperCamelCase      )				->    Optional[int]:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,							**UpperCamelCase__      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     shutil.rmtree(self.tmpdirname      )







   def 		UpperCamelCase__			(	self      )				->    Any:
     __a			=					self.get_tokenizer()
     __a			=					self.get_feature_extractor()

     __a			=					ClapProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__      )

     processor.save_pretrained(self.tmpdirname      )
     __a			=					ClapProcessor.from_pretrained(self.tmpdirname      )

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

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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					ClapProcessor(tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()      )
     processor.save_pretrained(self.tmpdirname      )

     __a			=					self.get_tokenizer(bos_token='(BOS)'      ,							eos_token='(EOS)'      )
     __a			=					self.get_feature_extractor(do_normalize=UpperCamelCase__      ,							padding_value=1.0      )

     __a			=					ClapProcessor.from_pretrained(
         self.tmpdirname      ,							bos_token='(BOS)'      ,							eos_token='(EOS)'      ,							do_normalize=UpperCamelCase__      ,							padding_value=1.0      )

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

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







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.get_feature_extractor()
     __a			=					self.get_tokenizer()

     __a			=					ClapProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__      )

     __a			=					floats_list((3, 1000)      )

     __a			=					feature_extractor(UpperCamelCase__      ,							return_tensors='np'      )
     __a			=					processor(audios=UpperCamelCase__      ,							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 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_feature_extractor()
     __a			=					self.get_tokenizer()

     __a			=					ClapProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__      )

     __a			=					'This is a test string'

     __a			=					processor(text=UpperCamelCase__      )

     __a			=					tokenizer(UpperCamelCase__      )

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







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

     __a			=					ClapProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__      )

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

     __a			=					processor.batch_decode(UpperCamelCase__      )
     __a			=					tokenizer.batch_decode(UpperCamelCase__      )

     self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					self.get_feature_extractor()
     __a			=					self.get_tokenizer()

     __a			=					ClapProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__      )

     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'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 10**12			):
  __a			=					1
  __a			=					0

  __a			=					1
  __a			=					1

  while numerator <= 2 * min_total - 1:
    prev_numerator += 2 * numerator
    numerator += 2 * prev_numerator

    prev_denominator += 2 * denominator
    denominator += 2 * prev_denominator

  return (denominator + 1) // 2


if __name__ == "__main__":
  print(f"""{solution() = }""")



