



'''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					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  List[Any]											= "laion/clap-htsat-unfused"
      lowerCAmelCase__			:  Tuple											= tempfile.mkdtemp()




  def 							UpperCAmelCase_     (   self  ,**__UpperCAmelCase				)   ->		int:
      return RobertaTokenizer.from_pretrained(self.checkpoint  ,**_SCREAMING_SNAKE_CASE				)




  def 							UpperCAmelCase_     (   self  ,**__UpperCAmelCase				)   ->		Optional[int]:
      return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,**_SCREAMING_SNAKE_CASE				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      shutil.rmtree(self.tmpdirname				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  List[Any]											= self.get_tokenizer()
      lowerCAmelCase__			:  List[Any]											= self.get_feature_extractor()

      lowerCAmelCase__			:  Dict											= ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,feature_extractor=_SCREAMING_SNAKE_CASE				)

      processor.save_pretrained(self.tmpdirname				)
      lowerCAmelCase__			:  List[Any]											= ClapProcessor.from_pretrained(self.tmpdirname				)

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

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




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  str											= ClapProcessor(tokenizer=self.get_tokenizer()  ,feature_extractor=self.get_feature_extractor()				)
      processor.save_pretrained(self.tmpdirname				)

      lowerCAmelCase__			:  Optional[int]											= self.get_tokenizer(bos_token="""(BOS)"""  ,eos_token="""(EOS)"""				)
      lowerCAmelCase__			:  Optional[Any]											= self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE  ,padding_value=1.0				)

      lowerCAmelCase__			:  Dict											= ClapProcessor.from_pretrained(
          self.tmpdirname  ,bos_token="""(BOS)"""  ,eos_token="""(EOS)"""  ,do_normalize=_SCREAMING_SNAKE_CASE  ,padding_value=1.0				)

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

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




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  str											= self.get_feature_extractor()
      lowerCAmelCase__			:  Tuple											= self.get_tokenizer()

      lowerCAmelCase__			:  Any											= ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,feature_extractor=_SCREAMING_SNAKE_CASE				)

      lowerCAmelCase__			:  List[Any]											= floats_list((3, 1000)				)

      lowerCAmelCase__			:  Optional[Any]											= feature_extractor(_SCREAMING_SNAKE_CASE  ,return_tensors="""np"""				)
      lowerCAmelCase__			:  List[str]											= processor(audios=_SCREAMING_SNAKE_CASE  ,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				)   ->		Optional[int]:
      lowerCAmelCase__			:  int											= self.get_feature_extractor()
      lowerCAmelCase__			:  str											= self.get_tokenizer()

      lowerCAmelCase__			:  str											= ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,feature_extractor=_SCREAMING_SNAKE_CASE				)

      lowerCAmelCase__			:  Tuple											= "This is a test string"

      lowerCAmelCase__			:  str											= processor(text=_SCREAMING_SNAKE_CASE				)

      lowerCAmelCase__			:  Tuple											= tokenizer(_SCREAMING_SNAKE_CASE				)

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




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  Optional[int]											= self.get_feature_extractor()
      lowerCAmelCase__			:  Union[str, Any]											= self.get_tokenizer()

      lowerCAmelCase__			:  Any											= ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,feature_extractor=_SCREAMING_SNAKE_CASE				)

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

      lowerCAmelCase__			:  Tuple											= processor.batch_decode(_SCREAMING_SNAKE_CASE				)
      lowerCAmelCase__			:  int											= tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)

      self.assertListEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE				)






  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Tuple											= self.get_feature_extractor()
      lowerCAmelCase__			:  str											= self.get_tokenizer()

      lowerCAmelCase__			:  Any											= ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,feature_extractor=_SCREAMING_SNAKE_CASE				)

      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  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations(UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item      ) for item in array      )

    return count_of_possible_combinations(UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase							,   UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        lowerCAmelCase__			:  Any											= sum(
            count_of_possible_combinations_with_dp_array(target - item							,   UpperCamelCase      )
            for item in array      )
        lowerCAmelCase__			:  Tuple											= answer
        return answer

    lowerCAmelCase__			:  Optional[int]											= [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,   UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= [0] * (target + 1)
    lowerCAmelCase__			:  List[Any]											= 1

    for i in range(1							,   target + 1      ):
        for j in range(UpperCamelCase      ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCAmelCase										=     3
 _lowerCAmelCase										=     5
 _lowerCAmelCase										=     [1, 2, 5]
 print(combination_sum_iv(n, array, target))


