






'''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 ..auto import CONFIG_MAPPING


_A	:			Dict         =logging.get_logger(__name__)

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

class _lowercase	(				_lowercase		):
							a				      =       """table-transformer"""
							a				      =       ["""past_key_values"""]
							a				      =       {
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}




							def __init__(							self:				Dict			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				str=None			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				Any=100			,							UpperCamelCase__:				str=6			,							UpperCamelCase__:				Tuple=2_048			,							UpperCamelCase__:				Any=8			,							UpperCamelCase__:				List[str]=6			,							UpperCamelCase__:				Union[str, Any]=2_048			,							UpperCamelCase__:				Dict=8			,							UpperCamelCase__:				List[Any]=0.0			,							UpperCamelCase__:				Optional[int]=0.0			,							UpperCamelCase__:				Dict=True			,							UpperCamelCase__:				List[str]="relu"			,							UpperCamelCase__:				Optional[Any]=256			,							UpperCamelCase__:				Dict=0.1			,							UpperCamelCase__:				Optional[Any]=0.0			,							UpperCamelCase__:				str=0.0			,							UpperCamelCase__:				List[str]=0.02			,							UpperCamelCase__:				Union[str, Any]=1.0			,							UpperCamelCase__:				Dict=False			,							UpperCamelCase__:				Dict="sine"			,							UpperCamelCase__:				str="resnet50"			,							UpperCamelCase__:				List[str]=True			,							UpperCamelCase__:				Tuple=False			,							UpperCamelCase__:				List[str]=1			,							UpperCamelCase__:				Tuple=5			,							UpperCamelCase__:				Any=2			,							UpperCamelCase__:				Optional[int]=1			,							UpperCamelCase__:				Tuple=1			,							UpperCamelCase__:				List[Any]=5			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				int=0.1			,							**UpperCamelCase__:				Optional[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."""     )
																												lowerCamelCase__					:	str         =       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]     )
																					elif isinstance(UpperCamelCase__			,							UpperCamelCase__     ):
																												lowerCamelCase__					:	Optional[int]         =       backbone_config.get("""model_type"""     )
																												lowerCamelCase__					:	Tuple         =       CONFIG_MAPPING[backbone_model_type]
																												lowerCamelCase__					:	str         =       config_class.from_dict(UpperCamelCase__     )
																					# set timm attributes to None
																					lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       None, None, None

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




							@property
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														return self.encoder_attention_heads




							@property
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														return self.d_model



class _lowercase	(				_lowercase		):
							a				      =       version.parse("""1.11"""		)




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




							@property
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														return 1e-5




							@property
							def 		lowerCamelCase_				(							self:				Tuple     ):
														return 12









'''simple docstring'''







def  SCREAMING_SNAKE_CASE_       (UpperCamelCase = 1000      )      ->       int:
							lowerCamelCase__					:	str         =       -1
							lowerCamelCase__					:	Dict         =       0
							for a in range(1				,			n // 3      ):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														lowerCamelCase__					:	Dict         =       (n * n - 2 * a * n) // (2 * n - 2 * a)
														lowerCamelCase__					:	Any         =       n - a - b
														if c * c == (a * a + b * b):
																					lowerCamelCase__					:	Dict         =       a * b * c
																					if candidate >= product:
																												lowerCamelCase__					:	Union[str, Any]         =       candidate
							return product


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

