


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


__a     =			logging.get_logger(__name__)






@dataclass
class   __lowercase				(  __snake_case  ):
  UpperCamelCase								=       [
      'no_inference',
      'no_cuda',
      'no_tpu',
      'no_speed',
      'no_memory',
      'no_env_print',
      'no_multi_process',
  ]





  def __init__(		self						:					Union[str, Any]					,       **__lowerCamelCase						:					Optional[int]       )    ->   Union[str, Any]:






     """simple docstring"""







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

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





  @cached_property
  def        _lowercase				(		self						:					List[str]       )    ->   Dict:






     """simple docstring"""







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





  @cached_property
  def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






     """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       )

        UpperCAmelCase							=     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"""       )
           UpperCAmelCase							=     tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""       )
        else:
           tf.config.set_visible_devices([]					,       """GPU"""       )  # disable GPU
           UpperCAmelCase							=     tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""       )

     return strategy





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






     """simple docstring"""







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





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






     """simple docstring"""







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





  @property
  def        _lowercase				(		self						:					List[str]       )    ->   Optional[Any]:






     """simple docstring"""







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





  @property
  def        _lowercase				(		self						:					List[str]       )    ->   Optional[Any]:






     """simple docstring"""







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





  @property
  def        _lowercase				(		self						:					int       )    ->   Optional[Any]:






     """simple docstring"""







     return self.n_gpu > 0







from math import isqrt
def   _UpperCamelCase				(						lowerCAmelCase_   )						->bool:
   return all(number % divisor != 0 for divisor in range(2  ,						isqrt(lowerCAmelCase_   ) + 1   )   )
def   _UpperCamelCase				(						lowerCAmelCase_ = 1_0**6   )						->int:
   UpperCAmelCase							=     0
   UpperCAmelCase							=     1
   UpperCAmelCase							=     7
   while prime_candidate < max_prime:
      primes_count += is_prime(lowerCAmelCase_   )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


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



