


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  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			str:
             a_					:		Any							=  'laion/clap-htsat-unfused'
             a_					:		Tuple							=  tempfile.mkdtemp()
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							**SCREAMING_SNAKE_CASE__				:       Dict     )		->			List[Any]:
             return RobertaTokenizer.from_pretrained(self.checkpoint     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							**SCREAMING_SNAKE_CASE__				:       int     )		->			int:
             return ClapFeatureExtractor.from_pretrained(self.checkpoint     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[Any]:
             shutil.rmtree(self.tmpdirname     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Optional[Any]:
             a_					:		Optional[int]							=  self.get_tokenizer()
             a_					:		int							=  self.get_feature_extractor()

             a_					:		Union[str, Any]							=  ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							feature_extractor=SCREAMING_SNAKE_CASE__     )

             processor.save_pretrained(self.tmpdirname     )
             a_					:		Optional[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        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             a_					:		Any							=  ClapProcessor(tokenizer=self.get_tokenizer()     ,							feature_extractor=self.get_feature_extractor()     )
             processor.save_pretrained(self.tmpdirname     )

             a_					:		Optional[int]							=  self.get_tokenizer(bos_token='(BOS)'     ,							eos_token='(EOS)'     )
             a_					:		int							=  self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE__     ,							padding_value=1.0     )

             a_					:		Optional[Any]							=  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        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Any:
             a_					:		Union[str, Any]							=  self.get_feature_extractor()
             a_					:		Union[str, Any]							=  self.get_tokenizer()

             a_					:		Optional[Any]							=  ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							feature_extractor=SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  floats_list((3, 1_0_0_0)     )

             a_					:		Any							=  feature_extractor(SCREAMING_SNAKE_CASE__     ,							return_tensors='np'     )
             a_					:		List[Any]							=  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        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			int:
             a_					:		Union[str, Any]							=  self.get_feature_extractor()
             a_					:		Dict							=  self.get_tokenizer()

             a_					:		Tuple							=  ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							feature_extractor=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  'This is a test string'

             a_					:		List[str]							=  processor(text=SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  tokenizer(SCREAMING_SNAKE_CASE__     )

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]     ,							encoded_processor[key]     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Any:
             a_					:		Tuple							=  self.get_feature_extractor()
             a_					:		Any							=  self.get_tokenizer()

             a_					:		Dict							=  ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							feature_extractor=SCREAMING_SNAKE_CASE__     )

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

             a_					:		List[str]							=  processor.batch_decode(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__     )

             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[Any]:
             a_					:		Dict							=  self.get_feature_extractor()
             a_					:		Optional[Any]							=  self.get_tokenizer()

             a_					:		Optional[int]							=  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 lowercase_  (   _snake_case   ,_snake_case						):

							if not (isinstance(_snake_case   ,_snake_case						) and isinstance(_snake_case   ,_snake_case						)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""						)

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   len(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       int           =   len(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   [[0] * (texta_length + 1) for _ in range(texta_length + 1						)]
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   0
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   0

							for i in range(1   ,texta_length + 1						):
														for j in range(1   ,texta_length + 1						):
																					if texta[i - 1] == texta[j - 1]:
																												SCREAMING_SNAKE_CASE__						:       int           =   1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			SCREAMING_SNAKE_CASE__						:       List[Any]           =   i
																																			SCREAMING_SNAKE_CASE__						:       List[str]           =   dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
					import doctest

					doctest.testmod()




