

'''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						    =      logging.get_logger(__name__)

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


class    A__ ( _snake_case     ):
    lowercase		   =						"table-transformer"
    lowercase		   =						["past_key_values"]
    lowercase		   =						{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }



    def __init__(   self						,			UpperCamelCase__=True						,			UpperCamelCase__=None						,			UpperCamelCase__=3						,			UpperCamelCase__=100						,			UpperCamelCase__=6						,			UpperCamelCase__=2048						,			UpperCamelCase__=8						,			UpperCamelCase__=6						,			UpperCamelCase__=2048						,			UpperCamelCase__=8						,			UpperCamelCase__=0.0						,			UpperCamelCase__=0.0						,			UpperCamelCase__=True						,			UpperCamelCase__="relu"						,			UpperCamelCase__=256						,			UpperCamelCase__=0.1						,			UpperCamelCase__=0.0						,			UpperCamelCase__=0.0						,			UpperCamelCase__=0.02						,			UpperCamelCase__=1.0						,			UpperCamelCase__=False						,			UpperCamelCase__="sine"						,			UpperCamelCase__="resnet50"						,			UpperCamelCase__=True						,			UpperCamelCase__=False						,			UpperCamelCase__=1						,			UpperCamelCase__=5						,			UpperCamelCase__=2						,			UpperCamelCase__=1						,			UpperCamelCase__=1						,			UpperCamelCase__=5						,			UpperCamelCase__=2						,			UpperCamelCase__=0.1						,			**UpperCamelCase__						,			)  -> str:





           '''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."""    )
                         A_		   = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
                  elif isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                         A_		   = backbone_config.get("""model_type"""    )
                         A_		   = CONFIG_MAPPING[backbone_model_type]
                         A_		   = config_class.from_dict(UpperCamelCase__    )
                  # 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=UpperCamelCase__						,			**UpperCamelCase__    )



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           return self.encoder_attention_heads







    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           return self.d_model


class    A__ ( _snake_case     ):
    lowercase		   =						version.parse("1.11"     )



    @property
    def    snake_case_     (   self    )  -> Mapping[str, Mapping[int, str]]:





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



    @property
    def    snake_case_     (   self    )  -> float:





           '''simple docstring'''
           return 1e-5







    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           return 12



'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__ = 10_00			)     ->     int:
       return sum(2 * a * ((a - 1) // 2) for a in range(3,    n + 1			)			)


if __name__ == "__main__":
      print(solution())
