





'''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    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      "laion/clap-htsat-unfused"
     _lowerCAmelCase				=      tempfile.mkdtemp()




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Optional[Any]:
     return RobertaTokenizer.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Dict:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Tuple:
     shutil.rmtree(self.tmpdirname		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      self.get_tokenizer()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      ClapProcessor(tokenizer=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     processor.save_pretrained(self.tmpdirname		)
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      ClapProcessor(tokenizer=self.get_tokenizer()			,     feature_extractor=self.get_feature_extractor()		)
     processor.save_pretrained(self.tmpdirname		)

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

     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      self.get_feature_extractor()
     _lowerCAmelCase				=      self.get_tokenizer()

     _lowerCAmelCase				=      ClapProcessor(tokenizer=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      floats_list((3, 1000)		)

     _lowerCAmelCase				=      feature_extractor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      self.get_feature_extractor()
     _lowerCAmelCase				=      self.get_tokenizer()

     _lowerCAmelCase				=      ClapProcessor(tokenizer=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      "This is a test string"

     _lowerCAmelCase				=      processor(text=_lowerCAmelCase		)

     _lowerCAmelCase				=      tokenizer(_lowerCAmelCase		)

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




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      self.get_feature_extractor()
     _lowerCAmelCase				=      self.get_tokenizer()

     _lowerCAmelCase				=      ClapProcessor(tokenizer=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

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

     _lowerCAmelCase				=      processor.batch_decode(_lowerCAmelCase		)
     _lowerCAmelCase				=      tokenizer.batch_decode(_lowerCAmelCase		)

     self.assertListEqual(_lowerCAmelCase			,     _lowerCAmelCase		)





 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.get_feature_extractor()
     _lowerCAmelCase				=      self.get_tokenizer()

     _lowerCAmelCase				=      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"			,     )











'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


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


