






"""simple docstring"""
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


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

if is_tf_available():
      import tensorflow as tf



@dataclass
class 						a_ (      _a					):







      '''simple docstring'''

      lowerCamelCase__			:							List[str]					=      'tf'
      lowerCamelCase__			:							Dict					=      field(
          default=_a    ,		metadata={'help': 'Name of TPU'}    ,		)

      lowerCamelCase__			:							Dict					=      field(
          default=_a    ,		metadata={'help': 'Zone of TPU'}    ,		)

      lowerCamelCase__			:							List[str]					=      field(
          default=_a    ,		metadata={'help': 'Name of Cloud TPU-enabled project'}    ,		)

      lowerCamelCase__			:							int					=      field(
          default=1.0    ,		metadata={'help': 'Power for the Polynomial decay LR scheduler.'}    ,		)

      lowerCamelCase__			:							Optional[int]					=      field(default=_a    ,		metadata={'help': 'Whether to activate the XLA compilation or not'}					)




      @cached_property
      def      a__				(self   ):

       '''simple docstring'''



       requires_backends(self,	['tf']   )
       logger.info('Tensorflow: setting up strategy'   )

       lowerCamelCase__	:     Tuple								=		tf.config.list_physical_devices('GPU'   )

       # Set to float16 at first
       if self.fpaa:
        tf.keras.mixed_precision.set_global_policy('mixed_float16'   )

       if self.no_cuda:
        lowerCamelCase__	:     Tuple								=		tf.distribute.OneDeviceStrategy(device='/cpu:0'   )
       else:
        try:
         if self.tpu_name:
          lowerCamelCase__	:     int								=		tf.distribute.cluster_resolver.TPUClusterResolver(
              self.tpu_name,	zone=self.tpu_zone,	project=self.gcp_project   )
         else:
          lowerCamelCase__	:     int								=		tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
         if self.tpu_name:
          raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''   )
         else:
          lowerCamelCase__	:     Any								=		None

        if tpu:
         # Set to bfloat16 in case of TPU
         if self.fpaa:
          tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'   )

         tf.config.experimental_connect_to_cluster(_SCREAMING_SNAKE_CASE   )
         tf.tpu.experimental.initialize_tpu_system(_SCREAMING_SNAKE_CASE   )

         lowerCamelCase__	:     Dict								=		tf.distribute.TPUStrategy(_SCREAMING_SNAKE_CASE   )

        elif len(_SCREAMING_SNAKE_CASE   ) == 0:
         lowerCamelCase__	:     List[str]								=		tf.distribute.OneDeviceStrategy(device='/cpu:0'   )
        elif len(_SCREAMING_SNAKE_CASE   ) == 1:
         lowerCamelCase__	:     List[Any]								=		tf.distribute.OneDeviceStrategy(device='/gpu:0'   )
        elif len(_SCREAMING_SNAKE_CASE   ) > 1:
         # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
         lowerCamelCase__	:     Optional[Any]								=		tf.distribute.MirroredStrategy()
        else:
         raise ValueError('Cannot find the proper strategy, please check your environment properties.'   )

       return strategy




      @property
      def      a__				(self   ):

       '''simple docstring'''



       requires_backends(self,	['tf']   )
       return self._setup_strategy




      @property
      def      a__				(self   ):

       '''simple docstring'''



       requires_backends(self,	['tf']   )
       return self._setup_strategy.num_replicas_in_sync




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return False  # TF Logging is handled by Keras not the Trainer




      @property
      def      a__				(self   ):

       '''simple docstring'''



       if self.per_gpu_train_batch_size:
        logger.warning(
            'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
            'version. Using `--per_device_train_batch_size` is preferred.'   )
       lowerCamelCase__	:     Union[str, Any]								=		self.per_gpu_train_batch_size or self.per_device_train_batch_size
       return per_device_batch_size * self.n_replicas




      @property
      def      a__				(self   ):

       '''simple docstring'''



       if self.per_gpu_eval_batch_size:
        logger.warning(
            'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
            'version. Using `--per_device_eval_batch_size` is preferred.'   )
       lowerCamelCase__	:     List[str]								=		self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
       return per_device_batch_size * self.n_replicas





      @property
      def      a__				(self   ):

       '''simple docstring'''



       requires_backends(self,	['tf']   )
       warnings.warn(
           'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.',	_SCREAMING_SNAKE_CASE,	)
       return self._setup_strategy.num_replicas_in_sync



'''simple docstring'''


import math
from collections.abc import Generator
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)	))
						for i in (n for n in numbers if n > 1):
												for j in range(2							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)	))
						for i in (n for n in numbers if n > 1):
												# only need to check for factors up to sqrt(i)
												UpperCamelCase_     :	List[Any]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(2							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)							,				2	))
						# It's useless to test even numbers as they will not be prime
						if max_n > 2:
												yield 2  # Because 2 will not be tested, it's necessary to yield it now
						for i in (n for n in numbers if n > 1):
												UpperCamelCase_     :	Optional[int]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(3							,				UpperCAmelCase__							,				2	):
																		# As we removed the even numbers, we don't need them now
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       ):
						from timeit import timeit

						UpperCamelCase_     :	int					     =     """from __main__ import slow_primes, primes, fast_primes"""
						print(timeit("""slow_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""fast_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      int(input("Calculate primes up to:\n>> ").strip())
	for ret in primes(number):
		print(ret)
	benchmark()




