



'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


a							   =				logging.get_logger(__name__)

a							   =				{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Union[str, Any]        =				"""detr"""
							UpperCAmelCase			:		Tuple        =				["""past_key_values"""]
							UpperCAmelCase			:		int        =				{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}


							def __init__(				self   :						Any      ,						a_   :						str=True      ,						a_   :						str=None      ,						a_   :						Dict=3      ,						a_   :						Any=1_0_0      ,						a_   :						Optional[Any]=6      ,						a_   :						List[Any]=2_0_4_8      ,						a_   :						str=8      ,						a_   :						Any=6      ,						a_   :						List[str]=2_0_4_8      ,						a_   :						Any=8      ,						a_   :						str=0.0      ,						a_   :						int=0.0      ,						a_   :						Optional[Any]=True      ,						a_   :						int="relu"      ,						a_   :						str=2_5_6      ,						a_   :						int=0.1      ,						a_   :						Any=0.0      ,						a_   :						Tuple=0.0      ,						a_   :						Union[str, Any]=0.0_2      ,						a_   :						List[str]=1.0      ,						a_   :						Union[str, Any]=False      ,						a_   :						Dict="sine"      ,						a_   :						int="resnet50"      ,						a_   :						Optional[int]=True      ,						a_   :						List[Any]=False      ,						a_   :						Any=1      ,						a_   :						Optional[int]=5      ,						a_   :						Tuple=2      ,						a_   :						int=1      ,						a_   :						Tuple=1      ,						a_   :						List[Any]=5      ,						a_   :						List[str]=2      ,						a_   :						Optional[int]=0.1      ,						**a_   :						str      ,						)			->					Union[str, Any]:
										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.'			)
																snake_case:    Union[str, Any]      							=CONFIG_MAPPING['resnet'](out_features=['stage4']			)
													elif isinstance(a_      ,						a_			):
																snake_case:    List[Any]      							=backbone_config.get('model_type'			)
																snake_case:    Tuple      							=CONFIG_MAPPING[backbone_model_type]
																snake_case:    List[str]      							=config_class.from_dict(a_			)
													# set timm attributes to None
													snake_case							,  snake_case							,  snake_case:    Tuple      							=None, None, None

										snake_case:    Optional[Any]      							=use_timm_backbone
										snake_case:    Tuple      							=backbone_config
										snake_case:    Tuple      							=num_channels
										snake_case:    Any      							=num_queries
										snake_case:    Tuple      							=d_model
										snake_case:    Tuple      							=encoder_ffn_dim
										snake_case:    Dict      							=encoder_layers
										snake_case:    Tuple      							=encoder_attention_heads
										snake_case:    Tuple      							=decoder_ffn_dim
										snake_case:    Optional[int]      							=decoder_layers
										snake_case:    Optional[int]      							=decoder_attention_heads
										snake_case:    str      							=dropout
										snake_case:    int      							=attention_dropout
										snake_case:    Dict      							=activation_dropout
										snake_case:    Optional[int]      							=activation_function
										snake_case:    str      							=init_std
										snake_case:    Union[str, Any]      							=init_xavier_std
										snake_case:    Union[str, Any]      							=encoder_layerdrop
										snake_case:    Tuple      							=decoder_layerdrop
										snake_case:    List[Any]      							=encoder_layers
										snake_case:    List[Any]      							=auxiliary_loss
										snake_case:    Optional[Any]      							=position_embedding_type
										snake_case:    List[Any]      							=backbone
										snake_case:    int      							=use_pretrained_backbone
										snake_case:    Any      							=dilation
										# Hungarian matcher
										snake_case:    Optional[Any]      							=class_cost
										snake_case:    Tuple      							=bbox_cost
										snake_case:    List[Any]      							=giou_cost
										# Loss coefficients
										snake_case:    List[str]      							=mask_loss_coefficient
										snake_case:    int      							=dice_loss_coefficient
										snake_case:    Dict      							=bbox_loss_coefficient
										snake_case:    Optional[int]      							=giou_loss_coefficient
										snake_case:    Any      							=eos_coefficient
										super().__init__(is_encoder_decoder=a_      ,						**a_			)


							@property
							def    UpperCamelCase     (				self   :						Any			)			->					int:
										return self.encoder_attention_heads


							@property
							def    UpperCamelCase     (				self   :						List[Any]			)			->					int:
										return self.d_model


							@classmethod
							def    UpperCamelCase     (				cls   :						Any      ,						a_   :						PretrainedConfig      ,						**a_   :						int			)			->					int:
										return cls(backbone_config=a_      ,						**a_			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict[str, any]:
										snake_case:    Any      							=copy.deepcopy(self.__dict__			)
										if output["backbone_config"] is not None:
													snake_case:    Tuple      							=self.backbone_config.to_dict()
										snake_case:    Optional[int]      							=self.__class__.model_type
										return output






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Optional[Any]        =				version.parse("""1.11"""    )


							@property
							def    UpperCamelCase     (				self   :						List[Any]			)			->					Mapping[str, Mapping[int, str]]:
										return OrderedDict(
										    [
										        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
										        ('pixel_mask', {0: 'batch'}),
										    ]			)


							@property
							def    UpperCamelCase     (				self   :						str			)			->					float:
										return 1E-5




							@property
							def    UpperCamelCase     (				self   :						Optional[int]			)			->					int:
										return 1_2









'''simple docstring'''

from __future__ import annotations







def a_     (    __UpperCAmelCase					)				->					list[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=[True] * limit
			snake_case:    Optional[int]      							=False
			snake_case:    Union[str, Any]      							=False
			snake_case:    List[Any]      							=True

			for i in range(3				,							int(limit**0.5 + 1					)				,							2					):
						snake_case:    str      							=i * 2
						while index < limit:
									snake_case:    List[Any]      							=False
									snake_case:    List[str]      							=index + i

			snake_case:    Union[str, Any]      							=[2]

			for i in range(3				,							__UpperCAmelCase				,							2					):
						if is_prime[i]:
									primes.append(__UpperCAmelCase					)

			return primes







def a_     (    __UpperCAmelCase = 1_00_00_00					)				->					int:





			"""simple docstring"""







			snake_case:    str      							=prime_sieve(__UpperCAmelCase					)
			snake_case:    str      							=0
			snake_case:    str      							=0

			for i in range(len(__UpperCAmelCase					)					):
						for j in range(i + length				,							len(__UpperCAmelCase					)					):
									snake_case:    Tuple      							=sum(primes[i:j]					)
									if sol >= ceiling:
												break

									if sol in primes:
												snake_case:    List[str]      							=j - i
												snake_case:    Optional[int]      							=sol

			return largest


if __name__ == "__main__":
	print(F"""{solution() = }""")




