


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
		import torch

if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


lowerCAmelCase__    :str       =				logging.get_logger(__name__)

@dataclass
class 						__a (							UpperCAmelCase     ):
		_a      :    Optional[Any]           =      [
		    'no_inference',
		    'no_cuda',
		    'no_tpu',
		    'no_speed',
		    'no_memory',
		    'no_env_print',
		    'no_multi_process',
		]
		def __init__(     self     ,       **_SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





									for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																							_UpperCAmelCase											=     deprecated_arg[3:]
																							setattr(self     ,       _SCREAMING_SNAKE_CASE     ,       not kwargs.pop(_SCREAMING_SNAKE_CASE		)		)
																							logger.warning(
																							    f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
																							    f''' {positive_arg}={kwargs[positive_arg]}'''		)

									_UpperCAmelCase											=     kwargs.pop('torchscript'     ,       self.torchscript		)
									_UpperCAmelCase											=     kwargs.pop('torch_xla_tpu_print_metrics'     ,       self.torch_xla_tpu_print_metrics		)
									_UpperCAmelCase											=     kwargs.pop('fp16_opt_level'     ,       self.fpaa_opt_level		)
									super().__init__(**_SCREAMING_SNAKE_CASE		)

		_a      :    bool           =      field(default=UpperCAmelCase       , metadata={'help': 'Trace the models using torchscript'}     )
		_a      :    bool           =      field(default=UpperCAmelCase       , metadata={'help': 'Print Xla/PyTorch tpu metrics'}     )
		_a      :    str           =      field(
		    default='O1'       , metadata={
		        'help': (
		            'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
		            'See details at https://nvidia.github.io/apex/amp.html'
		        )
		    }       , )
		@cached_property
		def       UpperCAmelCase__							(     self		)					->			Tuple["torch.device", int]:




									"""simple docstring"""





									requires_backends(self     ,       ['torch']		)
									logger.info('PyTorch: setting up devices'		)
									if not self.cuda:
																_UpperCAmelCase											=     torch.device('cpu'		)
																_UpperCAmelCase											=     0
									elif is_torch_tpu_available():
																_UpperCAmelCase											=     xm.xla_device()
																_UpperCAmelCase											=     0
									else:
																_UpperCAmelCase											=     torch.device('cuda' if torch.cuda.is_available() else 'cpu'		)
																_UpperCAmelCase											=     torch.cuda.device_count()
									return device, n_gpu
		@property
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									return is_torch_tpu_available() and self.tpu
		@property
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									requires_backends(self     ,       ['torch']		)
									# TODO(PVP): currently only single GPU is supported
									return torch.cuda.current_device()
		@property
		def       UpperCAmelCase__							(     self		)					->			"torch.device":




									"""simple docstring"""





									requires_backends(self     ,       ['torch']		)
									return self._setup_devices[0]
		@property
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									requires_backends(self     ,       ['torch']		)
									return self._setup_devices[1]






		@property
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									return self.n_gpu > 0




def 			lowerCAmelCase__					(     a__:						list			)    ->						list:


							'''simple docstring'''





							if len(a__			) < 2:
														return collection

							def circle_sort_util(a__:						list     ,    a__:						int     ,    a__:						int			) -> bool:
														_UpperCAmelCase											=     False

														if low == high:
																					return swapped

														_UpperCAmelCase											=     low
														_UpperCAmelCase											=     high

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

																					left += 1
																					right -= 1

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

																					_UpperCAmelCase											=     True

														_UpperCAmelCase											=     low + int((high - low) / 2			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    a__     ,    a__			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    mid + 1     ,    a__			)

														return swapped or left_swap or right_swap

							_UpperCAmelCase											=     True

							while is_not_sorted is True:
														_UpperCAmelCase											=     circle_sort_util(a__     ,    0     ,    len(a__			) - 1			)

							return collection


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