

from __future__ import annotations

import math





def 	__a     (      A__			:  int       ):

    if num <= 0:
        SCREAMING_SNAKE_CASE										=   F"{num}: Invalid input, please enter a positive integer."
        raise ValueError(A__       )

    SCREAMING_SNAKE_CASE										=   [True] * (num + 1)
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   2
    SCREAMING_SNAKE_CASE										=   int(math.sqrt(A__       )       )

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(A__       )

            # Set multiples of start be False
            for i in range(start * start			,  num + 1			,  A__       ):
                if sieve[i] is True:
                    SCREAMING_SNAKE_CASE										=   False

        start += 1

    for j in range(end + 1			,  num + 1       ):
        if sieve[j] is True:
            prime.append(A__       )

    return prime


if __name__ == "__main__":
    print(prime_sieve(int(input('Enter a positive integer: ').strip())))


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__A			:					Optional[Any]						=			logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Whether to use SortishSampler or not."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }					,       )





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d