
def 							A_     (__a				):

					'''simple docstring'''





					if len(_lowerCamelCase				) < 2:
										return collection

					def circle_sort_util(__a				,   __a				,   __a				) -> bool:
										A_				 =						False

										if low == high:
															return swapped

										A_				 =						low
										A_				 =						high

										while left < right:
															if collection[left] > collection[right]:
																				A_				 =						(
																				    collection[right],
																				    collection[left],
																				)
																				A_				 =						True

															left += 1
															right -= 1

										if left == right and collection[left] > collection[right + 1]:
															A_				 =						(
															    collection[right + 1],
															    collection[left],
															)

															A_				 =						True

										A_				 =						low + int((high - low) / 2				)
										A_				 =						circle_sort_util(_lowerCamelCase				,   _lowerCamelCase				,   _lowerCamelCase				)
										A_				 =						circle_sort_util(_lowerCamelCase				,   mid + 1				,   _lowerCamelCase				)

										return swapped or left_swap or right_swap

					A_				 =						True

					while is_not_sorted is True:
										A_				 =						circle_sort_util(_lowerCamelCase				,   0				,   len(_lowerCamelCase				) - 1				)

					return collection


if __name__ == "__main__":
		UpperCamelCase_					:						List[Any]				= input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase_					:						List[Any]				= [int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))






"""simple docstring"""





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch

if is_datasets_available():
		from datasets import load_dataset

UpperCamelCase_					:						Union[str, Any]				= random.Random()
def 							A_     (__a				,   __a=1.0				,   __a=None				,   __a=None				):

					'''simple docstring'''





					if rng is None:
										A_				 =						global_rng

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

					return values



class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					Union[str, Any]=7       ,   _snake_case						:					Any=400       ,   _snake_case						:					Tuple=2_000       ,   _snake_case						:					str=2_048       ,   _snake_case						:					List[Any]=128       ,   _snake_case						:					List[Any]=1       ,   _snake_case						:					Union[str, Any]=512       ,   _snake_case						:					List[str]=30       ,   _snake_case						:					List[str]=44_100       ,   )      -> Optional[int]:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						min_seq_length
								A_				 =						max_seq_length
								A_				 =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								A_				 =						spectrogram_length
								A_				 =						feature_size
								A_				 =						num_audio_channels
								A_				 =						hop_length
								A_				 =						chunk_length
								A_				 =						sampling_rate




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}






			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					str=False       ,   _snake_case						:					Optional[Any]=False	)      -> List[str]:







								"""simple docstring"""
								def _flatten(_snake_case						:					List[Any]	):
													return list(itertools.chain(*_snake_case	)	)

								if equal_length:
													A_				 =						[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
								else:
													# make sure that inputs increase in size
													A_				 =						[
													    floats_list((x, self.feature_size)	)
													    for x in range(self.min_seq_length       ,   self.max_seq_length       ,   self.seq_length_diff	)
													]
								if numpify:
													A_				 =						[np.asarray(_snake_case	) for x in speech_inputs]
								return speech_inputs





@require_torch
@require_torchaudio
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TvltFeatureExtractor




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TvltFeatureExtractionTester(self	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "spectrogram_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "feature_size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "num_audio_channels"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "hop_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "chunk_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "sampling_rate"	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						feat_extract_first.save_pretrained(_snake_case	)[0]
													check_json_file_has_correct_format(_snake_case	)
													A_				 =						self.feature_extraction_class.from_pretrained(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						os.path.join(_snake_case       ,   "feat_extract.json"	)
													feat_extract_first.to_json_file(_snake_case	)
													A_				 =						self.feature_extraction_class.from_json_file(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								# Initialize feature_extractor
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								# create three inputs of length 800, 1000, and 1200
								A_				 =						[floats_list((1, x)	)[0] for x in range(800       ,   1_400       ,   200	)]
								A_				 =						[np.asarray(_snake_case	) for speech_input in speech_inputs]

								# Test not batched input
								A_				 =						feature_extractor(np_speech_inputs[0]       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test batched
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test audio masking
								A_				 =						feature_extractor(
								    _snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100       ,   mask_audio=_snake_case	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test 2-D numpy arrays are batched.
								A_				 =						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
								A_				 =						np.asarray(_snake_case	)
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values
								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					int	)      -> List[str]:







								"""simple docstring"""
								A_				 =						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,   "clean"       ,   split="validation"	)
								# automatic decoding with librispeech
								A_				 =						ds.sort("id"	).select(range(_snake_case	)	)[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]






			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						self._load_datasamples(1	)
								A_				 =						TvltFeatureExtractor()
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="pt"	).audio_values

								self.assertEquals(audio_values.shape       ,   (1, 1, 192, 128)	)

								A_				 =						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]	)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,   _snake_case       ,   atol=1e-4	)	)




