





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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

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

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     List[str]		     =      "table-transformer"
     __snake_case   :     Any		     =      ["past_key_values"]
     __snake_case   :     Optional[Any]		     =      {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self						:   List[str]						,lowerCamelCase__						:   Optional[int]=True						,lowerCamelCase__						:   List[str]=None						,lowerCamelCase__						:   Optional[int]=3						,lowerCamelCase__						:   Optional[int]=100						,lowerCamelCase__						:   Optional[int]=6						,lowerCamelCase__						:   Optional[int]=2048						,lowerCamelCase__						:   List[str]=8						,lowerCamelCase__						:   List[str]=6						,lowerCamelCase__						:   Dict=2048						,lowerCamelCase__						:   int=8						,lowerCamelCase__						:   Dict=0.0						,lowerCamelCase__						:   Optional[Any]=0.0						,lowerCamelCase__						:   int=True						,lowerCamelCase__						:   Optional[Any]="relu"						,lowerCamelCase__						:   int=256						,lowerCamelCase__						:   List[str]=0.1						,lowerCamelCase__						:   int=0.0						,lowerCamelCase__						:   int=0.0						,lowerCamelCase__						:   List[str]=0.02						,lowerCamelCase__						:   int=1.0						,lowerCamelCase__						:   str=False						,lowerCamelCase__						:   Any="sine"						,lowerCamelCase__						:   Union[str, Any]="resnet50"						,lowerCamelCase__						:   Any=True						,lowerCamelCase__						:   Union[str, Any]=False						,lowerCamelCase__						:   Optional[int]=1						,lowerCamelCase__						:   Tuple=5						,lowerCamelCase__						:   Optional[Any]=2						,lowerCamelCase__						:   int=1						,lowerCamelCase__						:   int=1						,lowerCamelCase__						:   Union[str, Any]=5						,lowerCamelCase__						:   Optional[int]=2						,lowerCamelCase__						:   Optional[int]=0.1						,**lowerCamelCase__						:   List[Any]						,)					->		Any:




           '''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."""	)
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]	)
                 elif isinstance(lowerCamelCase__						,lowerCamelCase__	):
                       SCREAMING_SNAKE_CASE		=						backbone_config.get("""model_type"""	)
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE		=						config_class.from_dict(lowerCamelCase__	)
                 # set timm attributes to None
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						None, None, None

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






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		int:




           '''simple docstring'''


           return self.d_model

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     int		     =      version.parse("1.11"       )






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		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     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		float:




           '''simple docstring'''


           return 1e-5






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		int:




           '''simple docstring'''


           return 12









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 50     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[1] * (length + 1)

      for row_length in range(3			,						length + 1     ):
            for block_length in range(3			,						row_length + 1     ):
                  for block_start in range(row_length - block_length     ):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


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


