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


_lowercase							:      str      							=logging.get_logger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					_UpperCAmelCase			):
		'''simple docstring'''


		lowercase		:     List[str]            =				[
		    "no_inference",
		    "no_cuda",
		    "no_tpu",
		    "no_speed",
		    "no_memory",
		    "no_env_print",
		    "no_multi_process",
		]







		def __init__(				self    :					Optional[int]      ,	**SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				int:






									for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																							A						:     Optional[int]         =deprecated_arg[3:]
																							A						:     Union[str, Any]         =not kwargs.pop(lowercase__     )
																							logger.warning(
																							    f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
																							    f' {positive_arg}={kwargs[positive_arg]}'     )
									A						:     List[Any]         =kwargs.pop('tpu_name'      ,	self.tpu_name     )
									A						:     Optional[int]         =kwargs.pop('device_idx'      ,	self.device_idx     )
									A						:     int         =kwargs.pop('eager_mode'      ,	self.eager_mode     )
									A						:     str         =kwargs.pop('use_xla'      ,	self.use_xla     )
									super().__init__(**lowercase__     )

		lowercase		:     Any            =				field(
		    default=_UpperCAmelCase   ,						metadata={"help": "Name of TPU"}   ,						)
		lowercase		:     Dict            =				field(
		    default=0   ,						metadata={"help": "CPU / GPU device index. Defaults to 0."}   ,						)
		lowercase		:     List[Any]            =				field(default=_UpperCAmelCase   ,						metadata={"help": "Benchmark models in eager model."}			)
		lowercase		:     int            =				field(
		    default=_UpperCAmelCase   ,						metadata={
		        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
		    }   ,						)







		@cached_property
		def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Any:
									requires_backends(self      ,	['tf']     )
									A						:     Optional[Any]         =None
									if self.tpu:
																try:
																							if self.tpu_name:
																														A						:     Optional[int]         =tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
																							else:
																														A						:     Union[str, Any]         =tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																							A						:     str         =None
									return tpu







		@cached_property
		def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Dict:
									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						:     Any         =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						:     Dict         =tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'     )
																else:
																							tf.config.set_visible_devices([]      ,	'GPU'     )  # disable GPU
																							A						:     Optional[Any]         =tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'     )

									return strategy







		@property
		def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Optional[Any]:
									requires_backends(self      ,	['tf']     )
									return self._setup_tpu is not None







		@property
		def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Any:
									requires_backends(self      ,	['tf']     )
									return self._setup_strategy







		@property
		def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				List[str]:
									requires_backends(self      ,	['tf']     )
									return tf.config.list_physical_devices('GPU'     )







		@property
		def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
									requires_backends(self      ,	['tf']     )
									if self.cuda:
																return len(self.gpu_list     )
									return 0







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







import math



def A__  (			lowercase:     int					)				->		list:
       A						:     Optional[Any]         =[True] * n
       A						:     Tuple         =False
       A						:     List[Any]         =False
       A						:     Dict         =True

       for i in range(3,		int(n**0.5 + 1					),		2					):
              A						:     Dict         =i * 2
              while index < n:
                     A						:     Dict         =False
                     A						:     Dict         =index + i

       A						:     Tuple         =[2]

       for i in range(3,		lowercase,		2					):
              if is_prime[i]:
                     primes.append(lowercase					)

       return primes



def A__  (			lowercase:     int = 999_966_663_333					)				->		int:
       A						:     Optional[int]         =math.floor(math.sqrt(lowercase					)					) + 100
       A						:     Optional[int]         =prime_sieve(lowercase					)

       A						:     Optional[Any]         =0
       A						:     List[Any]         =0
       A						:     Union[str, Any]         =primes[prime_index]

       while (last_prime**2) <= limit:
              A						:     Tuple         =primes[prime_index + 1]

              A						:     Optional[int]         =last_prime**2
              A						:     Tuple         =next_prime**2

              # Get numbers divisible by lps(current)
              A						:     int         =lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A						:     List[Any]         =upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A						:     Any         =0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A						:     List[str]         =next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
    print(solution())






