
"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		list     )     ->						int:

    if not grid or not grid[0]:
        raise TypeError('The grid does not contain the appropriate information'     )

    for cell_n in range(1							,		len(grid[0]     )     ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    a__		=			grid[0]

    for row_n in range(1							,		len(UpperCamelCase     )     ):
        a__		=			grid[row_n]
        a__		=			fill_row(UpperCamelCase							,		UpperCamelCase     )
        a__		=			grid[row_n]

    return grid[-1][-1]



def     __magic_name__		(					UpperCamelCase     :		list							,		UpperCamelCase     :		list     )     ->						list:
    current_row[0] += row_above[0]
    for cell_n in range(1							,		len(UpperCamelCase     )     ):
        current_row[cell_n] += min(current_row[cell_n - 1]							,		row_above[cell_n]     )

    return current_row


if __name__ == "__main__":
    import doctest

    doctest.testmod()




"""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 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



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



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         shutil.rmtree(self.tmpdirname   )



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







         """simple docstring"""



         a__		=			self.get_tokenizer()
         a__		=			self.get_feature_extractor()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         processor.save_pretrained(self.tmpdirname   )
         a__		=			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 			lowercase__  (							self   )		->     Optional[int]:







         """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=__SCREAMING_SNAKE_CASE     ,				padding_value=1.0   )

         a__		=			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 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			floats_list((3, 1_0_0_0)   )

         a__		=			feature_extractor(__SCREAMING_SNAKE_CASE     ,				return_tensors='np'   )
         a__		=			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 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			'This is a test string'

         a__		=			processor(text=__SCREAMING_SNAKE_CASE   )

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE   )

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



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

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

         a__		=			processor.batch_decode(__SCREAMING_SNAKE_CASE   )
         a__		=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )

         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			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'     ,				)


