






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


__UpperCAmelCase							:   Optional[int]							=  logging.get_logger(__name__)


@dataclass
class 					_snake_case						(			_A ):
	_A          =   [
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]






	def __init__(		self					,**UpperCamelCase       )						->    Any:
					for deprecated_arg in self.deprecated_args:
									if deprecated_arg in kwargs:
													snake_case__      :List[Any]         =   deprecated_arg[3:]
													setattr(self					,A__					,not kwargs.pop(A__       )       )
													logger.warning(
													    f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
													    f' {positive_arg}={kwargs[positive_arg]}'       )

					snake_case__      :Union[str, Any]         =   kwargs.pop("torchscript"					,self.torchscript       )
					snake_case__      :Union[str, Any]         =   kwargs.pop("torch_xla_tpu_print_metrics"					,self.torch_xla_tpu_print_metrics       )
					snake_case__      :Optional[int]         =   kwargs.pop("fp16_opt_level"					,self.fpaa_opt_level       )
					super().__init__(**A__       )

	_A          =   field(default=_A       ,    metadata={'help': 'Trace the models using torchscript'} )
	_A          =   field(default=_A       ,    metadata={'help': 'Print Xla/PyTorch tpu metrics'} )
	_A          =   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    lowerCAmelCase_							(		self       )						->    List[str]:
					requires_backends(self					,["torch"]       )
					logger.info("PyTorch: setting up devices"       )
					if not self.cuda:
									snake_case__      :int         =   torch.device("cpu"       )
									snake_case__      :Dict         =   0
					elif is_torch_tpu_available():
									snake_case__      :Union[str, Any]         =   xm.xla_device()
									snake_case__      :str         =   0
					else:
									snake_case__      :Optional[int]         =   torch.device("cuda" if torch.cuda.is_available() else "cpu"       )
									snake_case__      :str         =   torch.cuda.device_count()
					return device, n_gpu






	@property
	def    lowerCAmelCase_							(		self       )						->    str:
					return is_torch_tpu_available() and self.tpu






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






	@property
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					requires_backends(self					,["torch"]       )
					return self._setup_devices[0]






	@property
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					requires_backends(self					,["torch"]       )
					return self._setup_devices[1]






	@property
	def    lowerCAmelCase_							(		self       )						->    Any:
					return self.n_gpu > 0







def 					lowercase_ (    __snake_case     :							list      )     -> list:





				'''simple docstring'''


				if any(not isinstance(__snake_case	,	__snake_case      ) or x < 0 for x in sequence      ):
								raise TypeError("Sequence must be list of non-negative integers"      )
				for _ in range(len(__snake_case      )      ):
								for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case	,	sequence[1:]      )      ):
												if rod_upper > rod_lower:
																sequence[i] -= rod_upper - rod_lower
																sequence[i + 1] += rod_upper - rod_lower
				return sequence


if __name__ == "__main__":
	assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]