






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


__A					=	"scheduler_config.json"



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							str				= 1
    lowerCamelCase	:							int				= 2
    lowerCamelCase	:							Any				= 3
    lowerCamelCase	:							Tuple				= 4
    lowerCamelCase	:							Dict				= 5
    lowerCamelCase	:							Optional[int]				= 6
    lowerCamelCase	:							Optional[Any]				= 7
    lowerCamelCase	:							Union[str, Any]				= 8
    lowerCamelCase	:							str				= 9
    lowerCamelCase	:							Union[str, Any]				= 10
    lowerCamelCase	:							Tuple				= 11
    lowerCamelCase	:							Dict				= 12
    lowerCamelCase	:							int				= 13
    lowerCamelCase	:							List[Any]				= 14




@dataclass
class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							torch.FloatTensor




class 				_A			:
    """simple docstring"""

    lowerCamelCase	:							Optional[Any]				= SCHEDULER_CONFIG_NAME
    lowerCamelCase	:							Dict				= []
    lowerCamelCase	:							Any				= True



    @classmethod
    def _a							(				cls      :     Any ,     __SCREAMING_SNAKE_CASE      :     Dict[str, Any] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[str] = None ,     __SCREAMING_SNAKE_CASE      :     Dict=False ,     **__SCREAMING_SNAKE_CASE      :     Tuple ,     )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=cls.load_config(
               pretrained_model_name_or_path=__SCREAMING_SNAKE_CASE ,     subfolder=__SCREAMING_SNAKE_CASE ,     return_unused_kwargs=__SCREAMING_SNAKE_CASE ,     return_commit_hash=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE ,     )
           return cls.from_config(__SCREAMING_SNAKE_CASE ,     return_unused_kwargs=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, os.PathLike] ,     __SCREAMING_SNAKE_CASE      :     bool = False ,     **__SCREAMING_SNAKE_CASE      :     Tuple      )      ->							Tuple:
           self.save_config(save_directory=__SCREAMING_SNAKE_CASE ,     push_to_hub=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    @property
    def _a							(				self      :     Union[str, Any]      )      ->							Any:
           return self._get_compatibles()



    @classmethod
    def _a							(				cls      :     int      )      ->							Tuple:
           __UpperCAmelCase     				=list(set([cls.__name__] + cls._compatibles      )      )
           __UpperCAmelCase     				=importlib.import_module(__name__.split("""."""      )[0]      )
           __UpperCAmelCase     				=[
               getattr(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) for c in compatible_classes_str if hasattr(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           ]
           return compatible_classes





'''simple docstring'''






import math





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  []
     _A    		=  2
     _A    		=  int(math.sqrt(__snake_case				)				)  # Size of every segment
     _A    		=  [True] * (end + 1)
     _A    		=  []

     while start <= end:
          if temp[start] is True:
               in_prime.append(__snake_case				)
               for i in range(start * start   ,		end + 1   ,		__snake_case				):
                    _A    		=  False
          start += 1
     prime += in_prime

     _A    		=  end + 1
     _A    		=  min(2 * end   ,		__snake_case				)

     while low <= n:
          _A    		=  [True] * (high - low + 1)
          for each in in_prime:
               _A    		=  math.floor(low / each				) * each
               if t < low:
                    t += each

               for j in range(__snake_case   ,		high + 1   ,		__snake_case				):
                    _A    		=  False

          for j in range(len(__snake_case				)				):
               if temp[j] is True:
                    prime.append(j + low				)

          _A    		=  high + 1
          _A    		=  min(high + end   ,		__snake_case				)

     return prime
print(sieve(10**6))



