





from __future__ import annotations

import math


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  List[Any]:

    if num <= 0:
        lowerCamelCase__					:   Union[str, Any]      				=	F"""{num}: Invalid input, please enter a positive integer."""
        raise ValueError(a_				)

    lowerCamelCase__					:   Any      				=	[True] * (num + 1)
    lowerCamelCase__					:   Union[str, Any]      				=	[]
    lowerCamelCase__					:   List[Any]      				=	2
    lowerCamelCase__					:   Dict      				=	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:
                    lowerCamelCase__					:   Optional[int]      				=	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())))












from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase       : Dict			     =       logging.get_logger(__name__)

_UpperCAmelCase       : Union[str, Any]			     =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	"""donut-swin"""

 UpperCAmelCase__									=	{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }
 def __init__(   self						:   Tuple    ,  UpperCAmelCase						:   List[str]=224    ,  UpperCAmelCase						:   Optional[Any]=4    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Union[str, Any]=96    ,  UpperCAmelCase						:   int=[2, 2, 6, 2]    ,  UpperCAmelCase						:   Dict=[3, 6, 12, 24]    ,  UpperCAmelCase						:   Any=7    ,  UpperCAmelCase						:   Optional[int]=4.0    ,  UpperCAmelCase						:   List[Any]=True    ,  UpperCAmelCase						:   Any=0.0    ,  UpperCAmelCase						:   Optional[Any]=0.0    ,  UpperCAmelCase						:   int=0.1    ,  UpperCAmelCase						:   List[str]="gelu"    ,  UpperCAmelCase						:   List[str]=False    ,  UpperCAmelCase						:   str=0.0_2    ,  UpperCAmelCase						:   str=1e-5    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			int:
     super().__init__(**UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	image_size
     lowerCamelCase__					:   Optional[int]      				=	patch_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embed_dim
     lowerCamelCase__					:   Optional[Any]      				=	depths
     lowerCamelCase__					:   Optional[int]      				=	len(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	num_heads
     lowerCamelCase__					:   Tuple      				=	window_size
     lowerCamelCase__					:   Dict      				=	mlp_ratio
     lowerCamelCase__					:   str      				=	qkv_bias
     lowerCamelCase__					:   Tuple      				=	hidden_dropout_prob
     lowerCamelCase__					:   str      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   str      				=	drop_path_rate
     lowerCamelCase__					:   int      				=	hidden_act
     lowerCamelCase__					:   str      				=	use_absolute_embeddings
     lowerCamelCase__					:   List[Any]      				=	layer_norm_eps
     lowerCamelCase__					:   Optional[Any]      				=	initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     lowerCamelCase__					:   Optional[int]      				=	int(embed_dim * 2 ** (len(UpperCAmelCase			) - 1)			)





