'''simple docstring'''




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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowercase        =    logging.get_logger(__name__)

__lowercase        =    {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 							a__(   lowerCAmelCase__					,		lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Tuple						=			'''swin'''

			UpperCAmelCase_     :	Optional[Any]						=			{
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}



			def __init__(  self			,       __lowerCAmelCase=224			,       __lowerCAmelCase=4			,       __lowerCAmelCase=3			,       __lowerCAmelCase=96			,       __lowerCAmelCase=[2, 2, 6, 2]			,       __lowerCAmelCase=[3, 6, 12, 24]			,       __lowerCAmelCase=7			,       __lowerCAmelCase=4.0			,       __lowerCAmelCase=True			,       __lowerCAmelCase=0.0			,       __lowerCAmelCase=0.0			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase="gelu"			,       __lowerCAmelCase=False			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=1E-5			,       __lowerCAmelCase=32			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)

					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    patch_size
					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    embed_dim
					lowerCAmelCase     =    depths
					lowerCAmelCase     =    len(__lowerCAmelCase)
					lowerCAmelCase     =    num_heads
					lowerCAmelCase     =    window_size
					lowerCAmelCase     =    mlp_ratio
					lowerCAmelCase     =    qkv_bias
					lowerCAmelCase     =    hidden_dropout_prob
					lowerCAmelCase     =    attention_probs_dropout_prob
					lowerCAmelCase     =    drop_path_rate
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    use_absolute_embeddings
					lowerCAmelCase     =    layer_norm_eps
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    encoder_stride
					# 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     =    int(embed_dim * 2 ** (len(__lowerCAmelCase) - 1))
					lowerCAmelCase     =    ["""stem"""] + [f"stage{idx}" for idx in range(1			,       len(__lowerCAmelCase) + 1)]
					lowerCAmelCase   ,	lowerCAmelCase     =    get_aligned_output_features_output_indices(
					    out_features=__lowerCAmelCase			,       out_indices=__lowerCAmelCase			,       stage_names=self.stage_names)







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			version.parse('''1.11'''      )



			@property
			def 				a_			(  self):




					"""simple docstring"""


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







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 1E-4





'''simple docstring'''




from __future__ import annotations

import math






def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


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

		lowerCAmelCase     =    [True] * (num + 1)
		lowerCAmelCase     =    []
		lowerCAmelCase     =    2
		lowerCAmelCase     =    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     =    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())))




