



"""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


__snake_case							:   Tuple      =    logging.get_logger(__name__)



@dataclass
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	[
	    'no_inference',
	    'no_cuda',
	    'no_tpu',
	    'no_speed',
	    'no_memory',
	    'no_env_print',
	    'no_multi_process',
	]

	def __init__(     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		int)				->       List[Any]:

				"""simple docstring"""

				for deprecated_arg in self.deprecated_args:
							if deprecated_arg in kwargs:
										__lowerCAmelCase      :  Dict												=       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]}""")

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

	SCREAMING_SNAKE_CASE          =	field(default=__SCREAMING_SNAKE_CASE  ,		metadata={'help': 'Trace the models using torchscript'}							)
	SCREAMING_SNAKE_CASE          =	field(default=__SCREAMING_SNAKE_CASE  ,		metadata={'help': 'Print Xla/PyTorch tpu metrics'}							)
	SCREAMING_SNAKE_CASE          =	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 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Tuple["torch.device", int]:

				"""simple docstring"""

				requires_backends(self       ,			["torch"])
				logger.info("PyTorch: setting up devices")
				if not self.cuda:
							__lowerCAmelCase      :  Union[str, Any]												=       torch.device("cpu")
							__lowerCAmelCase      :  Any												=       0
				elif is_torch_tpu_available():
							__lowerCAmelCase      :  str												=       xm.xla_device()
							__lowerCAmelCase      :  Dict												=       0
				else:
							__lowerCAmelCase      :  int												=       torch.device("cuda" if torch.cuda.is_available() else "cpu")
							__lowerCAmelCase      :  str												=       torch.cuda.device_count()
				return device, n_gpu

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

				"""simple docstring"""

				return is_torch_tpu_available() and self.tpu

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				requires_backends(self       ,			["torch"])
				# TODO(PVP): currently only single GPU is supported
				return torch.cuda.current_device()

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       "torch.device":

				"""simple docstring"""

				requires_backends(self       ,			["torch"])
				return self._setup_devices[0]

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

				"""simple docstring"""

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







	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       str:

				"""simple docstring"""

				return self.n_gpu > 0




"""simple docstring"""






import os
from math import logaa


def 			_lowercase				(  __snake_case = "base_exp.txt"	)						->   int:
			__lowerCAmelCase      :  float												=       0
			__lowerCAmelCase      :  Any												=       0
			for i, line in enumerate(open(os.path.join(os.path.dirname(__snake_case	)					,__snake_case	)	)	):
						__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       list(map(__snake_case					,line.split(","	)	)	)
						if x * logaa(__snake_case	) > largest:
									__lowerCAmelCase      :  Tuple												=       x * logaa(__snake_case	)
									__lowerCAmelCase      :  Optional[Any]												=       i + 1
			return result


if __name__ == "__main__":
	print(solution())