




"""simple docstring"""



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
      import tensorflow as tf


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)




@dataclass
class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				[
        '''no_inference''',
        '''no_cuda''',
        '''no_tpu''',
        '''no_speed''',
        '''no_memory''',
        '''no_env_print''',
        '''no_multi_process''',
    ]




    def __init__(   self						:	Any     ,			**snake_case_						:	str				)				->							Dict:




     '''simple docstring'''

     for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
       A__	=    deprecated_arg[3:]
       A__	=    not kwargs.pop(snake_case_				)
       logger.warning(
           F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
           F""" {positive_arg}={kwargs[positive_arg]}"""				)
     A__	=    kwargs.pop("tpu_name"     ,			self.tpu_name				)
     A__	=    kwargs.pop("device_idx"     ,			self.device_idx				)
     A__	=    kwargs.pop("eager_mode"     ,			self.eager_mode				)
     A__	=    kwargs.pop("use_xla"     ,			self.use_xla				)
     super().__init__(**snake_case_				)

    lowercase__						 =				field(
        default=A_,       metadata={'''help''': '''Name of TPU'''},       )
    lowercase__						 =				field(
        default=0,       metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''},       )
    lowercase__						 =				field(default=A_,       metadata={'''help''': '''Benchmark models in eager model.'''}							)
    lowercase__						 =				field(
        default=A_,       metadata={
            '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
        },       )




    @cached_property
    def 							__magic_name__							(   self						:	Any				)				->							Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:




     '''simple docstring'''

     requires_backends(self     ,			["tf"]				)
     A__	=    None
     if self.tpu:
      try:
       if self.tpu_name:
        A__	=    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name				)
       else:
        A__	=    tf.distribute.cluster_resolver.TPUClusterResolver()
      except ValueError:
       A__	=    None
     return tpu




    @cached_property
    def 							__magic_name__							(   self						:	Optional[int]				)				->							Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:




     '''simple docstring'''

     requires_backends(self     ,			["tf"]				)
     if self.is_tpu:
      tf.config.experimental_connect_to_cluster(self._setup_tpu				)
      tf.tpu.experimental.initialize_tpu_system(self._setup_tpu				)

      A__	=    tf.distribute.TPUStrategy(self._setup_tpu				)
     else:
      # currently no multi gpu is allowed
      if self.is_gpu:
       # TODO: Currently only single GPU is supported
       tf.config.set_visible_devices(self.gpu_list[self.device_idx]     ,			"GPU"				)
       A__	=    tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""				)
      else:
       tf.config.set_visible_devices([]     ,			"GPU"				)  # disable GPU
       A__	=    tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""				)

     return strategy




    @property
    def 							__magic_name__							(   self						:	Tuple				)				->							bool:




     '''simple docstring'''

     requires_backends(self     ,			["tf"]				)
     return self._setup_tpu is not None




    @property
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							"tf.distribute.Strategy":




     '''simple docstring'''

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




    @property
    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[int]:




     '''simple docstring'''

     requires_backends(self     ,			["tf"]				)
     return tf.config.list_physical_devices("GPU"				)




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     requires_backends(self     ,			["tf"]				)
     if self.cuda:
      return len(self.gpu_list				)
     return 0




    @property
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							bool:




     '''simple docstring'''

     return self.n_gpu > 0











"""simple docstring"""



import random

from .binary_exp_mod import bin_exp_mod







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=10_00						)						->		Optional[Any]:
 if n < 2:
  return False

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

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

 # n - 1=d*(2**exp)
 A__	=    0
 while count < prec:
  A__	=    random.randint(2      ,			n - 1						)
  A__	=    bin_exp_mod(lowercase_      ,			lowercase_      ,			lowercase_						)
  if b != 1:
   A__	=    True
   for _ in range(lowercase_						):
    if b == n - 1:
     A__	=    False
     break
    A__	=    b * b
    b %= n
   if flag:
    return False
   count += 1
 return True


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	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)))





