




"""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


lowerCAmelCase							:			Tuple     							=							logging.get_logger(__name__)

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






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						"table-transformer"
				__UpperCamelCase			  =						["past_key_values"]
				__UpperCamelCase			  =						{
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}





				def __init__(       self		, _a=True		, _a=None		, _a=3		, _a=100		, _a=6		, _a=2_048		, _a=8		, _a=6		, _a=2_048		, _a=8		, _a=0.0		, _a=0.0		, _a=True		, _a="relu"		, _a=256		, _a=0.1		, _a=0.0		, _a=0.0		, _a=0.02		, _a=1.0		, _a=False		, _a="sine"		, _a="resnet50"		, _a=True		, _a=False		, _a=1		, _a=5		, _a=2		, _a=1		, _a=1		, _a=5		, _a=2		, _a=0.1		, **_a		, ):





							"""simple docstring"""
							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			      =					CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
										elif isinstance(_a		, _a ):
													lowerCamelCase			      =					backbone_config.get("""model_type""" )
													lowerCamelCase			      =					CONFIG_MAPPING[backbone_model_type]
													lowerCamelCase			      =					config_class.from_dict(_a )
										# set timm attributes to None
										lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					None, None, None

							lowerCamelCase			      =					use_timm_backbone
							lowerCamelCase			      =					backbone_config
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					num_queries
							lowerCamelCase			      =					d_model
							lowerCamelCase			      =					encoder_ffn_dim
							lowerCamelCase			      =					encoder_layers
							lowerCamelCase			      =					encoder_attention_heads
							lowerCamelCase			      =					decoder_ffn_dim
							lowerCamelCase			      =					decoder_layers
							lowerCamelCase			      =					decoder_attention_heads
							lowerCamelCase			      =					dropout
							lowerCamelCase			      =					attention_dropout
							lowerCamelCase			      =					activation_dropout
							lowerCamelCase			      =					activation_function
							lowerCamelCase			      =					init_std
							lowerCamelCase			      =					init_xavier_std
							lowerCamelCase			      =					encoder_layerdrop
							lowerCamelCase			      =					decoder_layerdrop
							lowerCamelCase			      =					encoder_layers
							lowerCamelCase			      =					auxiliary_loss
							lowerCamelCase			      =					position_embedding_type
							lowerCamelCase			      =					backbone
							lowerCamelCase			      =					use_pretrained_backbone
							lowerCamelCase			      =					dilation
							# Hungarian matcher
							lowerCamelCase			      =					class_cost
							lowerCamelCase			      =					bbox_cost
							lowerCamelCase			      =					giou_cost
							# Loss coefficients
							lowerCamelCase			      =					mask_loss_coefficient
							lowerCamelCase			      =					dice_loss_coefficient
							lowerCamelCase			      =					bbox_loss_coefficient
							lowerCamelCase			      =					giou_loss_coefficient
							lowerCamelCase			      =					eos_coefficient
							super().__init__(is_encoder_decoder=_a		, **_a )





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return self.encoder_attention_heads





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return self.d_model






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						version.parse("1.11"				)





				@property
				def    _lowerCAmelCase				(       self ):





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





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return 1e-5





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return 12









"""simple docstring"""




def     a__ (				snake_case__				)						->					bool:
			lowerCamelCase			      =					(1 + 24 * n) ** 0.5
			return ((1 + root) / 6) % 1 == 0



def     a__ (				snake_case__ = 50_00				)						->					int:
			lowerCamelCase			      =					[(i * (3 * i - 1)) // 2 for i in range(1       , snake_case__				)]
			for i, pentagonal_i in enumerate(snake_case__				):
						for j in range(snake_case__       , len(snake_case__				)				):
									lowerCamelCase			      =					pentagonal_nums[j]
									lowerCamelCase			      =					pentagonal_i + pentagonal_j
									lowerCamelCase			      =					pentagonal_j - pentagonal_i
									if is_pentagonal(snake_case__				) and is_pentagonal(snake_case__				):
												return b

			return -1


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



