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 :      Union[str, Any]										=				logging.get_logger(__name__)



@dataclass
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		[
					    "no_inference",
					    "no_cuda",
					    "no_tpu",
					    "no_speed",
					    "no_memory",
					    "no_env_print",
					    "no_multi_process",
					]



					def __init__( self					:  List[str]							,		**__A					:  List[Any]	):
						for deprecated_arg in self.deprecated_args:
							if deprecated_arg in kwargs:
								snake_case__			:				Dict       =  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__			:				str       =  kwargs.pop("torchscript"							,		self.torchscript	)
						snake_case__			:				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=UpperCamelCase_			,       metadata={"help": "Trace the models using torchscript"}						)
					a_     =		field(default=UpperCamelCase_			,       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 	_lowercase					( self					:  List[str]	):
						requires_backends(self							,		["torch"]	)
						logger.info("PyTorch: setting up devices"	)
						if not self.cuda:
							snake_case__			:				List[Any]       =  torch.device("cpu"	)
							snake_case__			:				List[Any]       =  0
						elif is_torch_tpu_available():
							snake_case__			:				Any       =  xm.xla_device()
							snake_case__			:				Optional[Any]       =  0
						else:
							snake_case__			:				List[str]       =  torch.device("cuda" if torch.cuda.is_available() else "cpu"	)
							snake_case__			:				Optional[Any]       =  torch.cuda.device_count()
						return device, n_gpu



					@property
					def 	_lowercase					( self					:  Optional[int]	):
						return is_torch_tpu_available() and self.tpu



					@property
					def 	_lowercase					( self					:  List[str]	):
						requires_backends(self							,		["torch"]	)
						# TODO(PVP): currently only single GPU is supported
						return torch.cuda.current_device()



					@property
					def 	_lowercase					( self					:  Optional[int]	):
						requires_backends(self							,		["torch"]	)
						return self._setup_devices[0]



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






					@property
					def 	_lowercase					( self					:  str	):
						return self.n_gpu > 0











import sys

__lowerCamelCase :      List[str]										=				(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str = N       ):
	snake_case__			:				Any       =  -sys.maxsize - 1
	for i in range(len(snake_case_       ) - 12       ):
		snake_case__			:				Tuple       =  1
		for j in range(13       ):
			product *= int(n[i + j]       )
		if product > largest_product:
			snake_case__			:				Dict       =  product
	return largest_product


if __name__ == "__main__":
	print(f"{solution() = }")



