

import math
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      list:





    '''simple docstring'''






    A__          =   [True] * n
    A__          =   False
    A__          =   False
    A__          =   True

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

    A__          =   [2]

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

    return primes
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int = 999966663333      )							->      int:





    '''simple docstring'''






    A__          =   math.floor(math.sqrt(SCREAMING_SNAKE_CASE__      )      ) + 100
    A__          =   prime_sieve(SCREAMING_SNAKE_CASE__      )

    A__          =   0
    A__          =   0
    A__          =   primes[prime_index]

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

        A__          =   last_prime**2
        A__          =   next_prime**2

        # Get numbers divisible by lps(current)
        A__          =   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__          =   upper_bound - next_prime
        while current > lower_bound:
            matches_sum += current
            current -= next_prime

        # Remove the numbers divisible by both ups and lps
        A__          =   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__          =   next_prime
        prime_index += 1

    return matches_sum


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




from typing import Dict

from .base import GenericTensor, Pipeline


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        if truncation is not None:
            if "truncation" in tokenize_kwargs:
                raise ValueError(
                    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'		)
            A__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    Any:





        '''simple docstring'''







        if return_tensors:
            return model_outputs[0]
        if self.framework == "pt":
            return model_outputs[0].tolist()
        elif self.framework == "tf":
            return model_outputs[0].numpy().tolist()


    def __call__(      self :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







        return super().__call__(*lowercase_,**lowercase_		)

