


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 ..auto import CONFIG_MAPPING


snake_case							:	List[Any]					      =     logging.get_logger(__name__)

snake_case							:	Optional[Any]					      =     {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   List[Any]   				=      '''table-transformer'''
					UpperCAmelCase__ :   int   				=      ['''past_key_values''']
					UpperCAmelCase__ :   Any   				=      {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}
					def __init__(				self       :Union[str, Any]      ,__snake_case       :Any=True      ,__snake_case       :Tuple=None      ,__snake_case       :Dict=3      ,__snake_case       :Union[str, Any]=1_00      ,__snake_case       :Optional[Any]=6      ,__snake_case       :str=20_48      ,__snake_case       :Dict=8      ,__snake_case       :Tuple=6      ,__snake_case       :List[str]=20_48      ,__snake_case       :int=8      ,__snake_case       :List[Any]=0.0      ,__snake_case       :Any=0.0      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Any="relu"      ,__snake_case       :List[Any]=2_56      ,__snake_case       :Tuple=0.1      ,__snake_case       :Union[str, Any]=0.0      ,__snake_case       :Union[str, Any]=0.0      ,__snake_case       :List[Any]=0.02      ,__snake_case       :int=1.0      ,__snake_case       :List[str]=False      ,__snake_case       :int="sine"      ,__snake_case       :Optional[int]="resnet50"      ,__snake_case       :Tuple=True      ,__snake_case       :List[str]=False      ,__snake_case       :List[str]=1      ,__snake_case       :Optional[Any]=5      ,__snake_case       :List[str]=2      ,__snake_case       :Any=1      ,__snake_case       :Dict=1      ,__snake_case       :Optional[Any]=5      ,__snake_case       :List[str]=2      ,__snake_case       :Union[str, Any]=0.1      ,**__snake_case       :List[str]      ,) ->   int:




												if backbone_config is not None and use_timm_backbone:
																			raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'				)

												if not use_timm_backbone:
																			if backbone_config is None:
																										logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'				)
																										a__					=					CONFIG_MAPPING['resnet'](out_features=['stage4']				)
																			elif isinstance(_snake_case      ,_snake_case				):
																										a__					=					backbone_config.get('model_type'				)
																										a__					=					CONFIG_MAPPING[backbone_model_type]
																										a__					=					config_class.from_dict(_snake_case				)
																			# set timm attributes to None
																			a__				, a__				, a__					=					None, None, None

												a__					=					use_timm_backbone
												a__					=					backbone_config
												a__					=					num_channels
												a__					=					num_queries
												a__					=					d_model
												a__					=					encoder_ffn_dim
												a__					=					encoder_layers
												a__					=					encoder_attention_heads
												a__					=					decoder_ffn_dim
												a__					=					decoder_layers
												a__					=					decoder_attention_heads
												a__					=					dropout
												a__					=					attention_dropout
												a__					=					activation_dropout
												a__					=					activation_function
												a__					=					init_std
												a__					=					init_xavier_std
												a__					=					encoder_layerdrop
												a__					=					decoder_layerdrop
												a__					=					encoder_layers
												a__					=					auxiliary_loss
												a__					=					position_embedding_type
												a__					=					backbone
												a__					=					use_pretrained_backbone
												a__					=					dilation
												# Hungarian matcher
												a__					=					class_cost
												a__					=					bbox_cost
												a__					=					giou_cost
												# Loss coefficients
												a__					=					mask_loss_coefficient
												a__					=					dice_loss_coefficient
												a__					=					bbox_loss_coefficient
												a__					=					giou_loss_coefficient
												a__					=					eos_coefficient
												super().__init__(is_encoder_decoder=_snake_case      ,**_snake_case				)
					@property
					def 			lowerCamelCase__(				self       :str				) ->   int:
												return self.encoder_attention_heads



					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   int:
												return self.d_model







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Dict   				=      version.parse('''1.11'''				)
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
												        ('pixel_mask', {0: 'batch'}),
												    ]				)
					@property
					def 			lowerCamelCase__(				self       :int				) ->   float:
												return 1E-5



					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   int:
												return 12








def 							__lowercase       (			__lowerCAmelCase							:    int       ):

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

							a__					=					[True] * (num + 1)

							a__					=					2
							while p * p <= num:
														if primes[p]:
																					for i in range(p * p ,   num + 1 ,   __lowerCAmelCase       ):
																												a__					=					False
														p += 1

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						snake_case							:	Optional[Any]					      =				int(input('''Enter a positive integer: ''').strip())
						print(prime_sieve_eratosthenes(user_num))




