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


lowercase_  : str								=					logging.get_logger(__name__)

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






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   "table-transformer"
    __a						=   ["past_key_values"]
    __a						=   {
        "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			,			)							->       Union[str, Any]:
     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__: Optional[Any]=   CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
      elif isinstance(lowerCAmelCase			,			lowerCAmelCase ):
       SCREAMING_SNAKE_CASE__: Optional[int]=   backbone_config.get('''model_type''' )
       SCREAMING_SNAKE_CASE__: int=   CONFIG_MAPPING[backbone_model_type]
       SCREAMING_SNAKE_CASE__: Union[str, Any]=   config_class.from_dict(lowerCAmelCase )
      # set timm attributes to None
      SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Union[str, Any]=   None, None, None

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



    @property
    def     UpperCamelCase_					(	self )							->       int:
     return self.d_model






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   version.parse("1.11"     )
    @property
    def     UpperCamelCase_					(	self )							->       Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ('''pixel_mask''', {0: '''batch'''}),
         ] )
    @property
    def     UpperCamelCase_					(	self )							->       float:
     return 1e-5



    @property
    def     UpperCamelCase_					(	self )							->       int:
     return 12






def 		a__      (		_UpperCamelCase      :			int				):
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   if decimal in (0, 1):  # Exit cases for the recursion
      return str(_UpperCamelCase				)
   __lowerCamelCase				,__lowerCamelCase    				=     divmod(_UpperCamelCase			,2				)
   return binary_recursive(_UpperCamelCase				) + str(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			str				):
   __lowerCamelCase    				=     str(_UpperCamelCase				).strip()
   if not number:
      raise ValueError('''No input value was provided'''				)
   __lowerCamelCase    				=     '''-''' if number.startswith('''-'''				) else ''''''
   __lowerCamelCase    				=     number.lstrip('''-'''				)
   if not number.isnumeric():
      raise ValueError('''Input value is not an integer'''				)
   return F"""{negative}0b{binary_recursive(int(_UpperCamelCase				)				)}"""


if __name__ == "__main__":
  from doctest import testmod

  testmod()






