


"""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   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"laion/clap-htsat-unfused"
										__lowerCAmelCase				    =			tempfile.mkdtemp()

							def 			snake_case				(     self    ,   **__a	):
										return RobertaTokenizer.from_pretrained(self.checkpoint    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,   **__a	)

							def 			snake_case				(     self	):
										shutil.rmtree(self.tmpdirname	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_tokenizer()
										__lowerCAmelCase				    =			self.get_feature_extractor()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

										processor.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			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 			snake_case				(     self	):
										__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=__a    ,   padding_value=1.0	)

										__lowerCAmelCase				    =			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 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_feature_extractor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

										__lowerCAmelCase				    =			floats_list((3, 10_00)	)

										__lowerCAmelCase				    =			feature_extractor(__a    ,   return_tensors="np"	)
										__lowerCAmelCase				    =			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 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_feature_extractor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

										__lowerCAmelCase				    =			"This is a test string"

										__lowerCAmelCase				    =			processor(text=__a	)

										__lowerCAmelCase				    =			tokenizer(__a	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_feature_extractor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

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

										__lowerCAmelCase				    =			processor.batch_decode(__a	)
										__lowerCAmelCase				    =			tokenizer.batch_decode(__a	)

										self.assertListEqual(__a    ,   __a	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_feature_extractor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			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"    ,   )






'''simple docstring'''







from __future__ import annotations

from random import choice





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   return choice(__a )





def 					UpperCAmelCase_		(__a      :    list[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Dict			=   random_pivot(__a )

   # partition based on pivot
   # linear time
   _a				:	Optional[int]			=   [e for e in lst if e < pivot]
   _a				:	List[str]			=   [e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__a ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__a ) < k - 1:
      return kth_number(__a ,	k - len(__a ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(__a ,	__a )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



