

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        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						"""laion/clap-htsat-unfused"""
						A__				:       List[str]	  =						tempfile.mkdtemp()






					def     __A     (					self       ,			**A__     ):
						return RobertaTokenizer.from_pretrained(self.checkpoint       ,			**A__     )






					def     __A     (					self       ,			**A__     ):
						return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,			**A__     )






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






					def     __A     (					self     ):
						A__				:       List[Any]	  =						self.get_tokenizer()
						A__				:       int	  =						self.get_feature_extractor()

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

						processor.save_pretrained(self.tmpdirname     )
						A__				:       List[Any]	  =						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     __A     (					self     ):
						A__				:       Optional[int]	  =						ClapProcessor(tokenizer=self.get_tokenizer()       ,			feature_extractor=self.get_feature_extractor()     )
						processor.save_pretrained(self.tmpdirname     )

						A__				:       Dict	  =						self.get_tokenizer(bos_token="""(BOS)"""       ,			eos_token="""(EOS)"""     )
						A__				:       Tuple	  =						self.get_feature_extractor(do_normalize=A__       ,			padding_value=1.0     )

						A__				:       int	  =						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     __A     (					self     ):
						A__				:       Any	  =						self.get_feature_extractor()
						A__				:       Optional[Any]	  =						self.get_tokenizer()

						A__				:       Optional[int]	  =						ClapProcessor(tokenizer=A__       ,			feature_extractor=A__     )

						A__				:       Tuple	  =						floats_list((3, 1000)     )

						A__				:       List[str]	  =						feature_extractor(A__       ,			return_tensors="""np"""     )
						A__				:       Optional[Any]	  =						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     __A     (					self     ):
						A__				:       List[str]	  =						self.get_feature_extractor()
						A__				:       int	  =						self.get_tokenizer()

						A__				:       str	  =						ClapProcessor(tokenizer=A__       ,			feature_extractor=A__     )

						A__				:       int	  =						"""This is a test string"""

						A__				:       List[Any]	  =						processor(text=A__     )

						A__				:       Optional[int]	  =						tokenizer(A__     )

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






					def     __A     (					self     ):
						A__				:       int	  =						self.get_feature_extractor()
						A__				:       List[Any]	  =						self.get_tokenizer()

						A__				:       Tuple	  =						ClapProcessor(tokenizer=A__       ,			feature_extractor=A__     )

						A__				:       List[str]	  =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						A__				:       Dict	  =						processor.batch_decode(A__     )
						A__				:       Dict	  =						tokenizer.batch_decode(A__     )

						self.assertListEqual(A__       ,			A__     )






					def     __A     (					self     ):
						A__				:       Any	  =						self.get_feature_extractor()
						A__				:       List[str]	  =						self.get_tokenizer()

						A__				:       str	  =						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"""       ,			)






def 		UpperCamelCase   (lowercase_:					list   )	-> list:
	A__				:       Union[str, Any]	  =						False
	while is_sorted is False:  # Until all the indices are traversed keep looping
		A__				:       Dict	  =						True
		for i in range(0       , len(lowercase_   ) - 1       , 2   ):  # iterating over all even indices
			if input_list[i] > input_list[i + 1]:
				A__  ,  A__				:       Dict	  =						input_list[i + 1], input_list[i]
				# swapping if elements not in order
				A__				:       Optional[Any]	  =						False

		for i in range(1       , len(lowercase_   ) - 1       , 2   ):  # iterating over all odd indices
			if input_list[i] > input_list[i + 1]:
				A__  ,  A__				:       Dict	  =						input_list[i + 1], input_list[i]
				# swapping if elements not in order
				A__				:       Optional[Any]	  =						False
	return input_list


if __name__ == "__main__":
	print('Enter list to be sorted')
	A_			:					Optional[int]    			=     [int(x) for x in input().split()]
	# inputing elements of the list in one line
	A_			:					Optional[int]    			=     odd_even_sort(input_list)
	print('The sorted list is')
	print(sorted_list)



