





'''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       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   "laion/clap-htsat-unfused"
			lowerCAmelCase     :    int						=   tempfile.mkdtemp()



	def   _lowerCamelCase    (   self		,      **a_	):
			return RobertaTokenizer.from_pretrained(self.checkpoint		,      **a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return ClapFeatureExtractor.from_pretrained(self.checkpoint		,      **a_	)



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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()
			lowerCAmelCase     :    int						=   self.get_feature_extractor()

			lowerCAmelCase     :    List[str]						=   ClapProcessor(tokenizer=a_		,      feature_extractor=a_	)

			processor.save_pretrained(self.tmpdirname	)
			lowerCAmelCase     :    Optional[int]						=   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   ClapProcessor(tokenizer=self.get_tokenizer()		,      feature_extractor=self.get_feature_extractor()	)
			processor.save_pretrained(self.tmpdirname	)

			lowerCAmelCase     :    List[str]						=   self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"	)
			lowerCAmelCase     :    Union[str, Any]						=   self.get_feature_extractor(do_normalize=a_		,      padding_value=1.0	)

			lowerCAmelCase     :    Optional[Any]						=   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.get_feature_extractor()
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()

			lowerCAmelCase     :    List[str]						=   ClapProcessor(tokenizer=a_		,      feature_extractor=a_	)

			lowerCAmelCase     :    Any						=   floats_list((3, 1_000)	)

			lowerCAmelCase     :    Any						=   feature_extractor(a_		,      return_tensors="np"	)
			lowerCAmelCase     :    Tuple						=   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   self.get_feature_extractor()
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()

			lowerCAmelCase     :    List[str]						=   ClapProcessor(tokenizer=a_		,      feature_extractor=a_	)

			lowerCAmelCase     :    List[str]						=   "This is a test string"

			lowerCAmelCase     :    Dict						=   processor(text=a_	)

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

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.get_feature_extractor()
			lowerCAmelCase     :    List[Any]						=   self.get_tokenizer()

			lowerCAmelCase     :    Any						=   ClapProcessor(tokenizer=a_		,      feature_extractor=a_	)

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

			lowerCAmelCase     :    Dict						=   processor.batch_decode(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   tokenizer.batch_decode(a_	)

			self.assertListEqual(a_		,      a_	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self.get_feature_extractor()
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()

			lowerCAmelCase     :    Optional[Any]						=   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'''






def      __A						(							a_						:  int      ,a_						:  int	):
		return abs(a_	) if a == 0 else greatest_common_divisor(b % a      ,a_	)






def      __A						(							a_						:  int      ,a_						:  int	):
		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   y, x % y
		return abs(a_	)






def      __A						(							):
		try:
				lowerCAmelCase     :    Tuple						=   input("Enter two integers separated by comma (,): "	).split(","	)
				lowerCAmelCase     :    Tuple						=   int(nums[0]	)
				lowerCAmelCase     :    Tuple						=   int(nums[1]	)
				print(
				    f'''greatest_common_divisor({num_a}, {num_a}) = '''
				    f'''{greatest_common_divisor(a_      ,a_	)}'''	)
				print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(a_      ,a_	)}'''	)
		except (IndexError, UnboundLocalError, ValueError):
				print("Wrong input"	)


if __name__ == "__main__":
					main()



