

"""simple docstring"""






import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
						pass

if is_speech_available():
						from transformers import EncodecFeatureExtractor, MusicgenProcessor


lowerCamelCase__			:					Dict							  =					random.Random()
def  UpperCamelCase   (     _lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  Optional[int]=1.0,					_lowerCAmelCase       :  Tuple=None,					_lowerCAmelCase       :  str=None	)			->     List[Any]:
		if rng is None:
				_UpperCAmelCase      :							List[str]       =   global_rng

		_UpperCAmelCase      :							Any       =   []
		for batch_idx in range(shape[0]	):
				values.append([]	)
				for _ in range(shape[1]	):
						values[-1].append(rng.random() * scale	)

		return values






@require_torch
@require_sentencepiece
class       _UpperCAmelCase     (						unittest.TestCase):
			def 							__snake_case	(						self       )      ->      List[str]:






					'''simple docstring'''







					_UpperCAmelCase      :							str       =   """facebook/musicgen-small"""
					_UpperCAmelCase      :							Tuple       =   tempfile.mkdtemp()
			def 							__snake_case	(						self ,						**_A       )      ->      int:






					'''simple docstring'''







					return TaTokenizer.from_pretrained(self.checkpoint ,						**_A       )
			def 							__snake_case	(						self ,						**_A       )      ->      Optional[int]:






					'''simple docstring'''







					return EncodecFeatureExtractor.from_pretrained(self.checkpoint ,						**_A       )
			def 							__snake_case	(						self       )      ->      Dict:






					'''simple docstring'''







					shutil.rmtree(self.tmpdirname       )
			def 							__snake_case	(						self       )      ->      Tuple:






					'''simple docstring'''







					_UpperCAmelCase      :							List[str]       =   self.get_tokenizer()
					_UpperCAmelCase      :							List[Any]       =   self.get_feature_extractor()

					_UpperCAmelCase      :							Dict       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

					processor.save_pretrained(self.tmpdirname       )
					_UpperCAmelCase      :							int       =   MusicgenProcessor.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       )      ->      Optional[int]:






					'''simple docstring'''







					_UpperCAmelCase      :							Union[str, Any]       =   MusicgenProcessor(tokenizer=self.get_tokenizer() ,						feature_extractor=self.get_feature_extractor()       )
					processor.save_pretrained(self.tmpdirname       )

					_UpperCAmelCase      :							List[str]       =   self.get_tokenizer(bos_token="""(BOS)""" ,						eos_token="""(EOS)"""       )
					_UpperCAmelCase      :							Dict       =   self.get_feature_extractor(do_normalize=_A ,						padding_value=1.0       )

					_UpperCAmelCase      :							Optional[Any]       =   MusicgenProcessor.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       )      ->      List[str]:






					'''simple docstring'''







					_UpperCAmelCase      :							Dict       =   self.get_feature_extractor()
					_UpperCAmelCase      :							Dict       =   self.get_tokenizer()

					_UpperCAmelCase      :							Any       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

					_UpperCAmelCase      :							int       =   floats_list((3, 10_00)       )

					_UpperCAmelCase      :							List[str]       =   feature_extractor(_A ,						return_tensors="""np"""       )
					_UpperCAmelCase      :							Tuple       =   processor(_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       )      ->      Any:






					'''simple docstring'''







					_UpperCAmelCase      :							List[str]       =   self.get_feature_extractor()
					_UpperCAmelCase      :							Optional[int]       =   self.get_tokenizer()

					_UpperCAmelCase      :							Any       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

					_UpperCAmelCase      :							Dict       =   """This is a test string"""

					_UpperCAmelCase      :							List[Any]       =   processor(text=_A       )

					_UpperCAmelCase      :							str       =   tokenizer(_A       )

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]       )
			def 							__snake_case	(						self       )      ->      List[Any]:






					'''simple docstring'''







					_UpperCAmelCase      :							Tuple       =   self.get_feature_extractor()
					_UpperCAmelCase      :							Optional[int]       =   self.get_tokenizer()

					_UpperCAmelCase      :							Optional[int]       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

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

					_UpperCAmelCase      :							Union[str, Any]       =   processor.batch_decode(sequences=_A       )
					_UpperCAmelCase      :							List[str]       =   tokenizer.batch_decode(_A       )

					self.assertListEqual(_A ,						_A       )
			def 							__snake_case	(						self       )      ->      List[Any]:






					'''simple docstring'''







					_UpperCAmelCase      :							Tuple       =   self.get_feature_extractor()
					_UpperCAmelCase      :							Tuple       =   self.get_tokenizer()

					_UpperCAmelCase      :							List[Any]       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

					self.assertListEqual(
					    processor.model_input_names ,						feature_extractor.model_input_names ,						msg="""`processor` and `feature_extractor` model input names do not match""" ,						)
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






					'''simple docstring'''







					_UpperCAmelCase      :							Optional[int]       =   self.get_feature_extractor(padding_side="""left"""       )
					_UpperCAmelCase      :							Optional[Any]       =   self.get_tokenizer()

					_UpperCAmelCase      :							Union[str, Any]       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

					_UpperCAmelCase      :							Any       =   [floats_list((1, x)       )[0] for x in range(5 ,						20 ,						5       )]
					_UpperCAmelCase      :							Optional[int]       =   processor(_A       ).padding_mask

					_UpperCAmelCase      :							Optional[Any]       =   np.asarray(floats_list((3, 20)       )       )[:, None, :]
					_UpperCAmelCase      :							Union[str, Any]       =   processor.batch_decode(_A ,						padding_mask=_A       )

					self.assertIsInstance(_A ,						_A       )

					for audio in decoded_audios:
							self.assertIsInstance(_A ,						np.ndarray       )

					self.assertTrue(decoded_audios[0].shape == (1, 10)       )
					self.assertTrue(decoded_audios[1].shape == (1, 15)       )
					self.assertTrue(decoded_audios[2].shape == (1, 20)       )








"""simple docstring"""






def  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     str:
	_UpperCAmelCase      :							Any       =   len(_lowerCAmelCase	)
	for _ in range(
	    0,					int(((no_of_elements - 1) / 2) + 1	)	):  # we don't need to traverse to end of list as
		for j in range(0,					no_of_elements - 1	):
			if (
			    lst[j + 1] < lst[j]
			):  # applying bubble sort algorithm from left to right (or forwards)
				_UpperCAmelCase      :							List[str]       =   lst[j + 1]
				_UpperCAmelCase      :							Optional[int]       =   lst[j]
				_UpperCAmelCase      :							Optional[int]       =   temp
			if (
			    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
			):  # applying bubble sort algorithm from right to left (or backwards)
				_UpperCAmelCase      :							Any       =   lst[no_of_elements - 1 - j]
				_UpperCAmelCase      :							Optional[Any]       =   lst[no_of_elements - 2 - j]
				_UpperCAmelCase      :							Optional[Any]       =   temp
	return lst


if __name__ == "__main__":
						print('''enter the list to be sorted''')
						lowerCamelCase__			:					List[str]							  =					[int(x) for x in input().split()]  # inputing elements of the list in one line
						lowerCamelCase__			:					int							  =					double_sort(lst)
						print('''the sorted list is''')
						print(sorted_lst)





