




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      :	int)						->Optional[Any]:





           '''simple docstring'''

           A__       =    '''laion/clap-htsat-unfused'''
           A__       =    tempfile.mkdtemp()


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int]					, **UpperCAmelCase__      :	str)						->Optional[Any]:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, **UpperCAmelCase__      :	List[Any])						->List[str]:





           '''simple docstring'''

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


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





           '''simple docstring'''

           shutil.rmtree(self.tmpdirname)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Tuple:





           '''simple docstring'''

           A__       =    self.get_tokenizer()
           A__       =    self.get_feature_extractor()

           A__       =    ClapProcessor(tokenizer=A_					, feature_extractor=A_)

           processor.save_pretrained(self.tmpdirname)
           A__       =    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)						->Optional[Any]:





           '''simple docstring'''

           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=A_					, padding_value=1.0)

           A__       =    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      :	Union[str, Any])						->List[str]:





           '''simple docstring'''

           A__       =    self.get_feature_extractor()
           A__       =    self.get_tokenizer()

           A__       =    ClapProcessor(tokenizer=A_					, feature_extractor=A_)

           A__       =    floats_list((3, 1_000))

           A__       =    feature_extractor(A_					, return_tensors='''np''')
           A__       =    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      :	Any)						->Optional[Any]:





           '''simple docstring'''

           A__       =    self.get_feature_extractor()
           A__       =    self.get_tokenizer()

           A__       =    ClapProcessor(tokenizer=A_					, feature_extractor=A_)

           A__       =    '''This is a test string'''

           A__       =    processor(text=A_)

           A__       =    tokenizer(A_)

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


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





           '''simple docstring'''

           A__       =    self.get_feature_extractor()
           A__       =    self.get_tokenizer()

           A__       =    ClapProcessor(tokenizer=A_					, feature_extractor=A_)

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

           A__       =    processor.batch_decode(A_)
           A__       =    tokenizer.batch_decode(A_)

           self.assertListEqual(A_					, A_)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Tuple:





           '''simple docstring'''

           A__       =    self.get_feature_extractor()
           A__       =    self.get_tokenizer()

           A__       =    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 		_lowercase (     lowercase__ = 2_0_0						):
      __lowerCAmelCase					:						Union[str, Any]					=					[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
      __lowerCAmelCase					:						Dict					=					[0] * (pence + 1)
      __lowerCAmelCase					:						Optional[int]					=					1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowercase__   ,	pence + 1   ,	1						):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(200) == 7_3682


