





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


UpperCamelCase__           =			logging.get_logger(__name__)

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

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Any      =							"""table-transformer"""
							snake_case  :   Optional[int]      =							["""past_key_values"""]
							snake_case  :   Union[str, Any]      =							{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}






							def __init__(    self	,			__lowerCAmelCase=True	,			__lowerCAmelCase=None	,			__lowerCAmelCase=3	,			__lowerCAmelCase=100	,			__lowerCAmelCase=6	,			__lowerCAmelCase=2048	,			__lowerCAmelCase=8	,			__lowerCAmelCase=6	,			__lowerCAmelCase=2048	,			__lowerCAmelCase=8	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=True	,			__lowerCAmelCase="relu"	,			__lowerCAmelCase=256	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=0.02	,			__lowerCAmelCase=1.0	,			__lowerCAmelCase=False	,			__lowerCAmelCase="sine"	,			__lowerCAmelCase="resnet50"	,			__lowerCAmelCase=True	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1	,			__lowerCAmelCase=5	,			__lowerCAmelCase=2	,			__lowerCAmelCase=1	,			__lowerCAmelCase=1	,			__lowerCAmelCase=5	,			__lowerCAmelCase=2	,			__lowerCAmelCase=0.1	,			**__lowerCAmelCase	,			):
														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."""	)
																												UpperCamelCase__					  =      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]	)
																					elif isinstance(__lowerCAmelCase	,			__lowerCAmelCase	):
																												UpperCamelCase__					  =      backbone_config.get("""model_type"""	)
																												UpperCamelCase__					  =      CONFIG_MAPPING[backbone_model_type]
																												UpperCamelCase__					  =      config_class.from_dict(__lowerCAmelCase	)
																					# set timm attributes to None
																					UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      None, None, None

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






							@property
							def 					_lowerCamelCase		(    self	):
														return self.encoder_attention_heads







							@property
							def 					_lowerCamelCase		(    self	):
														return self.d_model






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Any      =							version.parse("""1.11"""							)






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






							@property
							def 					_lowerCamelCase		(    self	):
														return 1E-5







							@property
							def 					_lowerCamelCase		(    self	):
														return 12











def      _UpperCamelCase  (a__ :int = 1000							):






							"""simple docstring"""





							UpperCamelCase__					  =      2**power
							UpperCamelCase__					  =      0
							while n:
														UpperCamelCase__  ,  UpperCamelCase__					  =      r + n % 10, n // 10
							return r


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))




