






"""simple docstring"""







from __future__ import annotations


class      UpperCamelCase		:






						def __init__(		self  :Dict			, __magic_name__  :str=None				)      ->Optional[int]:
										lowercase    :      Union[str, Any]       = data
										lowercase    :      Optional[Any]       = None






						def __repr__(		self  :Optional[Any]				)      ->Optional[Any]:
										lowercase    :      Optional[Any]       = []
										lowercase    :      List[Any]       = self
										while temp:
														string_rep.append(f"""{temp.data}"""				)
														lowercase    :      Union[str, Any]       = temp.next
										return "->".join(__magic_name__				)


def      UpperCamelCase       ( _A )     ->       Tuple:
				if not elements_list:
								raise Exception("""The Elements List is empty""" )

				lowercase    :      Optional[int]       = Node(elements_list[0] )
				for i in range(1  ,				len(_A ) ):
								lowercase    :      Any       = Node(elements_list[i] )
								lowercase    :      Union[str, Any]       = current.next
				return head


def      UpperCamelCase       ( _A )     ->       None:
				if head_node is not None and isinstance(_A  ,				_A ):
								print_reverse(head_node.next )
								print(head_node.data )


def      UpperCamelCase       ( )     ->       List[Any]:
				from doctest import testmod

				testmod()

				lowercase    :      Optional[Any]       = make_linked_list([14, 52, 14, 12, 43] )
				print("""Linked List:""" )
				print(_A )
				print("""Elements in Reverse:""" )
				print_reverse(_A )


if __name__ == "__main__":
				main()








"""simple docstring"""







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            =	logging.get_logger(__name__)


@dataclass
class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		[
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(		self  :Union[str, Any]			, **__magic_name__  :int				)      ->Any:
										for deprecated_arg in self.deprecated_args:
														if deprecated_arg in kwargs:
																		lowercase    :      int       = deprecated_arg[3:]
																		setattr(self			, __magic_name__			, not kwargs.pop(__magic_name__				)				)
																		logger.warning(
																		    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																		    f""" {positive_arg}={kwargs[positive_arg]}"""				)

										lowercase    :      List[str]       = kwargs.pop("""torchscript"""			, self.torchscript				)
										lowercase    :      Any       = kwargs.pop("""torch_xla_tpu_print_metrics"""			, self.torch_xla_tpu_print_metrics				)
										lowercase    :      Union[str, Any]       = kwargs.pop("""fp16_opt_level"""			, self.fpaa_opt_level				)
										super().__init__(**__magic_name__				)

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






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										return is_torch_tpu_available() and self.tpu






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






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






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






						@property
						def      __snake_case      (		self  :int				)      ->List[str]:
										return self.n_gpu > 0
