





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 :   List[Any]  			=  logging.get_logger(__name__)

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


class        __SCREAMING_SNAKE_CASE      (			a__ ):
       _UpperCAmelCase				:  List[Any]			   =   "table-transformer"
       _UpperCAmelCase				:  Dict			   =   ["past_key_values"]
       _UpperCAmelCase				:  Union[str, Any]			   =   {
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }







       def __init__(				self					:     Any		, A					:     Union[str, Any]=True		, A					:     Dict=None		, A					:     int=3		, A					:     Any=1_0_0		, A					:     List[Any]=6		, A					:     Tuple=2_0_4_8		, A					:     Any=8		, A					:     Dict=6		, A					:     Tuple=2_0_4_8		, A					:     int=8		, A					:     Optional[int]=0.0		, A					:     List[Any]=0.0		, A					:     List[Any]=True		, A					:     Optional[int]="relu"		, A					:     Union[str, Any]=2_5_6		, A					:     Any=0.1		, A					:     Tuple=0.0		, A					:     Optional[int]=0.0		, A					:     str=0.02		, A					:     Tuple=1.0		, A					:     Dict=False		, A					:     str="sine"		, A					:     str="resnet50"		, A					:     Any=True		, A					:     List[str]=False		, A					:     Any=1		, A					:     int=5		, A					:     Tuple=2		, A					:     Optional[int]=1		, A					:     Any=1		, A					:     Dict=5		, A					:     str=2		, A					:     Union[str, Any]=0.1		, **A					:     int		, )       ->Optional[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.'''      )
                lowerCamelCase__       :						int        =							CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']      )
             elif isinstance(_lowerCamelCase		, _lowerCamelCase      ):
                lowerCamelCase__       :						str        =							backbone_config.get('''model_type'''      )
                lowerCamelCase__       :						Optional[int]        =							CONFIG_MAPPING[backbone_model_type]
                lowerCamelCase__       :						List[str]        =							config_class.from_dict(_lowerCamelCase      )
             # set timm attributes to None
             lowerCamelCase__       :						Union[str, Any]        =							None, None, None

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







       @property
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[Any]:
          return self.encoder_attention_heads







       @property
       def 						__lowerCamelCase   (				self					:     Any      )       ->Dict:
          return self.d_model


class        __SCREAMING_SNAKE_CASE      (			a__ ):
       _UpperCAmelCase				:  Union[str, Any]			   =   version.parse("1.11" )







       @property
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          return OrderedDict(
              [
                  ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ('''pixel_mask''', {0: '''batch'''}),
              ]      )







       @property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[Any]:
          return 1e-5







       @property
       def 						__lowerCamelCase   (				self					:     str      )       ->List[Any]:
          return 1_2










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					) or number < 0:
      raise ValueError('''Input must be a non-negative integer'''					)

   lowerCamelCase__       :						List[str]        =							0
   while number:
      # This way we arrive at next set bit (next 1) instead of looping
      # through each bit and checking for 1s hence the
      # loop won't run 32 times it will only run the number of `1` times
      number &= number - 1
      count += 1
   return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()



