



'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"deformable_detr"
    lowercase   =						{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }




    def __init__(       self   :	Union[str, Any]					,	snake_case_   :	Any=True					,	snake_case_   :	int=None					,	snake_case_   :	Optional[Any]=3					,	snake_case_   :	Optional[int]=300					,	snake_case_   :	Dict=1_024					,	snake_case_   :	Dict=6					,	snake_case_   :	int=1_024					,	snake_case_   :	Any=8					,	snake_case_   :	Dict=6					,	snake_case_   :	List[str]=1_024					,	snake_case_   :	int=8					,	snake_case_   :	Union[str, Any]=0.0					,	snake_case_   :	str=True					,	snake_case_   :	int="relu"					,	snake_case_   :	str=256					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	str=0.0					,	snake_case_   :	List[Any]=0.0					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	str=1.0					,	snake_case_   :	Tuple=True					,	snake_case_   :	int=False					,	snake_case_   :	Optional[Any]="sine"					,	snake_case_   :	str="resnet50"					,	snake_case_   :	Union[str, Any]=True					,	snake_case_   :	Any=False					,	snake_case_   :	List[str]=4					,	snake_case_   :	Union[str, Any]=4					,	snake_case_   :	str=4					,	snake_case_   :	int=False					,	snake_case_   :	List[Any]=300					,	snake_case_   :	Optional[Any]=False					,	snake_case_   :	Tuple=1					,	snake_case_   :	int=5					,	snake_case_   :	Optional[Any]=2					,	snake_case_   :	List[Any]=1					,	snake_case_   :	str=1					,	snake_case_   :	int=5					,	snake_case_   :	str=2					,	snake_case_   :	List[str]=0.1					,	snake_case_   :	Any=0.25					,	snake_case_   :	Tuple=False					,	**snake_case_   :	str					,	):
          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."""		)
                      snake_case__    :       Optional[int]       		= CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]		)
                elif isinstance(snake_case_					,	snake_case_		):
                      snake_case__    :       str       		= backbone_config.get("""model_type"""		)
                      snake_case__    :       Tuple       		= CONFIG_MAPPING[backbone_model_type]
                      snake_case__    :       Union[str, Any]       		= config_class.from_dict(snake_case_		)
          snake_case__    :       Optional[Any]       		= use_timm_backbone
          snake_case__    :       Tuple       		= backbone_config
          snake_case__    :       Dict       		= num_channels
          snake_case__    :       List[str]       		= num_queries
          snake_case__    :       Optional[Any]       		= max_position_embeddings
          snake_case__    :       List[Any]       		= d_model
          snake_case__    :       Tuple       		= encoder_ffn_dim
          snake_case__    :       Optional[Any]       		= encoder_layers
          snake_case__    :       Union[str, Any]       		= encoder_attention_heads
          snake_case__    :       Optional[int]       		= decoder_ffn_dim
          snake_case__    :       str       		= decoder_layers
          snake_case__    :       Union[str, Any]       		= decoder_attention_heads
          snake_case__    :       Dict       		= dropout
          snake_case__    :       str       		= attention_dropout
          snake_case__    :       Optional[int]       		= activation_dropout
          snake_case__    :       int       		= activation_function
          snake_case__    :       Union[str, Any]       		= init_std
          snake_case__    :       Dict       		= init_xavier_std
          snake_case__    :       List[Any]       		= encoder_layerdrop
          snake_case__    :       Tuple       		= auxiliary_loss
          snake_case__    :       str       		= position_embedding_type
          snake_case__    :       str       		= backbone
          snake_case__    :       Any       		= use_pretrained_backbone
          snake_case__    :       List[Any]       		= dilation
          # deformable attributes
          snake_case__    :       str       		= num_feature_levels
          snake_case__    :       Any       		= encoder_n_points
          snake_case__    :       Dict       		= decoder_n_points
          snake_case__    :       str       		= two_stage
          snake_case__    :       List[str]       		= two_stage_num_proposals
          snake_case__    :       str       		= with_box_refine
          if two_stage is True and with_box_refine is False:
                raise ValueError("""If two_stage is True, with_box_refine must be True."""		)
          # Hungarian matcher
          snake_case__    :       Dict       		= class_cost
          snake_case__    :       Optional[Any]       		= bbox_cost
          snake_case__    :       List[str]       		= giou_cost
          # Loss coefficients
          snake_case__    :       List[Any]       		= mask_loss_coefficient
          snake_case__    :       Dict       		= dice_loss_coefficient
          snake_case__    :       Optional[Any]       		= bbox_loss_coefficient
          snake_case__    :       Optional[int]       		= giou_loss_coefficient
          snake_case__    :       str       		= eos_coefficient
          snake_case__    :       str       		= focal_alpha
          snake_case__    :       Optional[Any]       		= disable_custom_kernels
          super().__init__(is_encoder_decoder=snake_case_					,	**snake_case_		)




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          return self.encoder_attention_heads




    @property
    def lowerCamelCase			(       self   :	Any		):
          return self.d_model




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       List[Any]       		= copy.deepcopy(self.__dict__		)
          if self.backbone_config is not None:
                snake_case__    :       str       		= self.backbone_config.to_dict()
          snake_case__    :       Optional[int]       		= self.__class__.model_type
          return output





'''simple docstring'''





__a  			=						"Alexander Joslin"

import operator as op

from .stack import Stack





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       str       		= {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

      snake_case__    :       Stack[int]       		= Stack()
      snake_case__    :       Stack[str]       		= Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(_lowerCAmelCase						)						)
            elif i in operators:
                  # RULE 2
                  operator_stack.push(_lowerCAmelCase						)
            elif i == ")":
                  # RULE 4
                  snake_case__    :       Union[str, Any]       		= operator_stack.peek()
                  operator_stack.pop()
                  snake_case__    :       Any       		= operand_stack.peek()
                  operand_stack.pop()
                  snake_case__    :       str       		= operand_stack.peek()
                  operand_stack.pop()

                  snake_case__    :       Optional[Any]       		= operators[opr](_lowerCAmelCase	,  _lowerCAmelCase						)
                  operand_stack.push(_lowerCAmelCase						)

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
       __a  			=						"(5 + ((4 * 2) * (2 + 3)))"
       # answer = 45
       print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")
