





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  _a							(    _lowercase):
	_a  :  Optional[Any]				   =							[
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]




	def __init__( self						:		Optional[int]		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Any:
			for deprecated_arg in self.deprecated_args:
					if deprecated_arg in kwargs:
							lowerCAmelCase__       :      Optional[int]   					= 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__       :      Tuple   					= kwargs.pop('''fp16_opt_level'''		, self.fpaa_opt_level		)
			super().__init__(**_SCREAMING_SNAKE_CASE		)

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




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




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




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




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




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Union[str, Any]:
			return self.n_gpu > 0








import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_   (	_a     ,       _a=1_000	):




		"""simple docstring"""






		if n < 2:
				return False

		if n % 2 == 0:
				return n == 2

		# this means n is odd
		lowerCAmelCase__       :      int   					= n - 1
		lowerCAmelCase__       :      Any   					= 0
		while d % 2 == 0:
				d /= 2
				exp += 1

		# n - 1=d*(2**exp)
		lowerCAmelCase__       :      Optional[Any]   					= 0
		while count < prec:
				lowerCAmelCase__       :      Optional[Any]   					= random.randint(2     ,       n - 1	)
				lowerCAmelCase__       :      List[Any]   					= bin_exp_mod(_a     ,       _a     ,       _a	)
				if b != 1:
						lowerCAmelCase__       :      Dict   					= True
						for _ in range(_a	):
								if b == n - 1:
										lowerCAmelCase__       :      Union[str, Any]   					= False
										break
								lowerCAmelCase__       :      Tuple   					= b * b
								b %= n
						if flag:
								return False
						count += 1
		return True


if __name__ == "__main__":
		lowerCamelCase						=		abs(int(input('''Enter bound : ''').strip()))
		print('''Here\'s the list of primes:''')
		print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))




