import warnings
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						:   Any         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   Tuple         =			{
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'segformer'



							def __init__(						self				,    lowercase_=3				,    lowercase_=4				,    lowercase_=[2, 2, 2, 2]				,    lowercase_=[8, 4, 2, 1]				,    lowercase_=[32, 64, 160, 256]				,    lowercase_=[7, 3, 3, 3]				,    lowercase_=[4, 2, 2, 2]				,    lowercase_=[1, 2, 5, 8]				,    lowercase_=[4, 4, 4, 4]				,    lowercase_="gelu"				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.1				,    lowercase_=0.02				,    lowercase_=0.1				,    lowercase_=1e-6				,    lowercase_=256				,    lowercase_=255				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

													if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
																			warnings.warn(
																			    "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
																			    " removed, as the behaviour will default to that of reshape_last_stage = True."				,    lowercase_				,    )

													_snake_case						:					str    =   num_channels
													_snake_case						:					List[Any]    =   num_encoder_blocks
													_snake_case						:					Any    =   depths
													_snake_case						:					Optional[Any]    =   sr_ratios
													_snake_case						:					str    =   hidden_sizes
													_snake_case						:					int    =   patch_sizes
													_snake_case						:					Any    =   strides
													_snake_case						:					str    =   mlp_ratios
													_snake_case						:					Union[str, Any]    =   num_attention_heads
													_snake_case						:					Optional[int]    =   hidden_act
													_snake_case						:					Optional[int]    =   hidden_dropout_prob
													_snake_case						:					Tuple    =   attention_probs_dropout_prob
													_snake_case						:					List[str]    =   classifier_dropout_prob
													_snake_case						:					Optional[Any]    =   initializer_range
													_snake_case						:					Any    =   drop_path_rate
													_snake_case						:					Union[str, Any]    =   layer_norm_eps
													_snake_case						:					int    =   decoder_hidden_size
													_snake_case						:					Union[str, Any]    =   kwargs.get("reshape_last_stage"				,    lowercase_    )
													_snake_case						:					Optional[Any]    =   semantic_loss_ignore_index




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		version.parse('1.11'		)



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



							@property
							def 	UpperCamelCase	(						self    ):
													return 1e-4



							@property
							def 	UpperCamelCase	(						self    ):
													return 12
def  snake_case						(__lowercase						)	->	list[int]:

						'''simple docstring'''



						if num <= 0:
												raise ValueError("Input must be a positive integer"						)

						_snake_case						:					Any    =   [True] * (num + 1)

						_snake_case						:					str    =   2
						while p * p <= num:
												if primes[p]:
																		for i in range(p * p ,      num + 1 ,      __lowercase						):
																								_snake_case						:					Optional[int]    =   False
												p += 1

						return [prime for prime in range(2 ,      num + 1						) if primes[prime]]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__SCREAMING_SNAKE_CASE						:   Any         =			int(input('Enter a positive integer: ').strip())
				print(prime_sieve_eratosthenes(user_num))