from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__		:       Dict       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       Any       						=       {
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'levit'




  def __init__(   self  :						str  ,			a_  :						Optional[Any]=224  ,			a_  :						List[str]=3  ,			a_  :						Any=3  ,			a_  :						Any=2  ,			a_  :						Tuple=1  ,			a_  :						int=16  ,			a_  :						Optional[int]=[128, 256, 384]  ,			a_  :						Dict=[4, 8, 12]  ,			a_  :						List[str]=[4, 4, 4]  ,			a_  :						Any=[16, 16, 16]  ,			a_  :						Dict=0  ,			a_  :						Tuple=[2, 2, 2]  ,			a_  :						Union[str, Any]=[2, 2, 2]  ,			a_  :						Optional[Any]=0.02  ,			**a_  :						str  ,			)->							Any:




       """simple docstring"""
       super().__init__(**a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		image_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_channels
       SCREAMING_SNAKE_CASE__					:					Any      =		kernel_size
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		stride
       SCREAMING_SNAKE_CASE__					:					Any      =		padding
       SCREAMING_SNAKE_CASE__					:					Any      =		hidden_sizes
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		depths
       SCREAMING_SNAKE_CASE__					:					List[str]      =		key_dim
       SCREAMING_SNAKE_CASE__					:					int      =		drop_path_rate
       SCREAMING_SNAKE_CASE__					:					List[str]      =		patch_size
       SCREAMING_SNAKE_CASE__					:					List[str]      =		attention_ratio
       SCREAMING_SNAKE_CASE__					:					Tuple      =		mlp_ratio
       SCREAMING_SNAKE_CASE__					:					str      =		initializer_range
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		[
           ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]




class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  version.parse('1.11'    )




  @property
  def 						__lowercase(   self  :						str					)->							Mapping[str, Mapping[int, str]]:




       """simple docstring"""
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]					)




  @property
  def 						__lowercase(   self  :						Any					)->							float:




       """simple docstring"""
       return 1e-4










from __future__ import annotations

import math





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> list[int]:




    """simple docstring"""




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

    _A     =							[True] * (num + 1)
    _A     =							[]
    _A     =							2
    _A     =							int(math.sqrt(_SCREAMING_SNAKE_CASE				)				)

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

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

        start += 1

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

    return prime


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





