






"""simple docstring"""



def        _lowerCAmelCase						(							UpperCAmelCase__    :   int )						->int:
						if not isinstance(UpperCAmelCase__,  UpperCAmelCase__ ):
												raise TypeError("""Input value must be an 'int' type""" )

						A__  :					Tuple											=					0
						while number:
												position += 1
												number >>= 1

						return position


if __name__ == "__main__":
						import doctest

						doctest.testmod()














"""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_      =							logging.get_logger(__name__)

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






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							snake_case_       =							'table-transformer'
							snake_case_       =							['past_key_values']
							snake_case_       =							{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}


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

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


							@property
							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													return self.encoder_attention_heads


							@property
							def   _UpperCamelCase    (     self :  Dict							):
													'''simple docstring'''





													return self.d_model






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							snake_case_       =							version.parse('1.11'						)


							@property
							def   _UpperCamelCase    (     self :  Any							):
													'''simple docstring'''





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


							@property
							def   _UpperCamelCase    (     self :  Optional[int]							):
													'''simple docstring'''





													return 1e-5


							@property
							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													return 12






