




'''simple docstring'''


import copy
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/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}






class 		lowerCAmelCase_     (    UpperCAmelCase_				):







  '''simple docstring'''

  UpperCamelCase_       :	int				       =							"""conditional_detr"""
  UpperCamelCase_       :	Optional[Any]				       =							["""past_key_values"""]
  UpperCamelCase_       :	Union[str, Any]				       =							{
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }



  def __init__(      self							:				Optional[Any] ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=True ,   SCREAMING_SNAKE_CASE_							:				str=None ,   SCREAMING_SNAKE_CASE_							:				List[str]=3 ,   SCREAMING_SNAKE_CASE_							:				int=3_00 ,   SCREAMING_SNAKE_CASE_							:				List[str]=6 ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=20_48 ,   SCREAMING_SNAKE_CASE_							:				List[str]=8 ,   SCREAMING_SNAKE_CASE_							:				int=6 ,   SCREAMING_SNAKE_CASE_							:				Dict=20_48 ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=8 ,   SCREAMING_SNAKE_CASE_							:				Dict=0.0 ,   SCREAMING_SNAKE_CASE_							:				List[Any]=0.0 ,   SCREAMING_SNAKE_CASE_							:				Tuple=True ,   SCREAMING_SNAKE_CASE_							:				Any="relu" ,   SCREAMING_SNAKE_CASE_							:				List[Any]=2_56 ,   SCREAMING_SNAKE_CASE_							:				str=0.1 ,   SCREAMING_SNAKE_CASE_							:				int=0.0 ,   SCREAMING_SNAKE_CASE_							:				Optional[Any]=0.0 ,   SCREAMING_SNAKE_CASE_							:				Tuple=0.02 ,   SCREAMING_SNAKE_CASE_							:				Tuple=1.0 ,   SCREAMING_SNAKE_CASE_							:				Optional[Any]=False ,   SCREAMING_SNAKE_CASE_							:				List[Any]="sine" ,   SCREAMING_SNAKE_CASE_							:				int="resnet50" ,   SCREAMING_SNAKE_CASE_							:				str=True ,   SCREAMING_SNAKE_CASE_							:				Tuple=False ,   SCREAMING_SNAKE_CASE_							:				List[str]=2 ,   SCREAMING_SNAKE_CASE_							:				int=5 ,   SCREAMING_SNAKE_CASE_							:				List[Any]=2 ,   SCREAMING_SNAKE_CASE_							:				Any=1 ,   SCREAMING_SNAKE_CASE_							:				Optional[Any]=1 ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=2 ,   SCREAMING_SNAKE_CASE_							:				str=5 ,   SCREAMING_SNAKE_CASE_							:				List[str]=2 ,   SCREAMING_SNAKE_CASE_							:				int=0.25 ,   **SCREAMING_SNAKE_CASE_							:				str ,   )       ->						str:







       '''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:						Union[str, Any]						=						CONFIG_MAPPING["resnet"](out_features=['''stage4''']     )
            elif isinstance(_lowerCAmelCase ,   _lowerCAmelCase     ):
                 A:						Optional[int]						=						backbone_config.get('''model_type'''     )
                 A:						List[Any]						=						CONFIG_MAPPING[backbone_model_type]
                 A:						Tuple						=						config_class.from_dict(_lowerCAmelCase     )

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



  @property
  def  _snake_case			(      self							:				Union[str, Any]     )       ->						List[Any]:







       '''simple docstring'''



       return self.encoder_attention_heads



  @property
  def  _snake_case			(      self							:				Tuple     )       ->						Optional[Any]:







       '''simple docstring'''



       return self.d_model




  def  _snake_case			(      self							:				Tuple     )       ->						Optional[int]:







       '''simple docstring'''



       A:						Any						=						copy.deepcopy(self.__dict__     )
       if self.backbone_config is not None:
            A:						Union[str, Any]						=						self.backbone_config.to_dict()
       A:						List[Any]						=						self.__class__.model_type
       return output






class 		lowerCAmelCase_     (    UpperCAmelCase_				):







  '''simple docstring'''

  UpperCamelCase_       :	Optional[int]				       =							version.parse("""1.11"""				)



  @property
  def  _snake_case			(      self							:				List[Any]     )       ->						List[Any]:







       '''simple docstring'''



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



  @property
  def  _snake_case			(      self							:				List[Any]     )       ->						Tuple:







       '''simple docstring'''



       return 1E-5




  @property
  def  _snake_case			(      self							:				Tuple     )       ->						Union[str, Any]:







       '''simple docstring'''



       return 12












import os
from math import logaa



def 					UpperCAmelCase__     (   lowerCamelCase = "base_exp.txt"      ):
  lowercase			:float							=  0
  lowercase			:str							=  0
  for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase      ),	lowerCamelCase      )      )      ):
    lowercase		,			lowercase			:str							=  list(map(lowerCamelCase,	line.split(","      )      )      )
    if x * logaa(lowerCamelCase      ) > largest:
      lowercase			:Optional[Any]							=  x * logaa(lowerCamelCase      )
      lowercase			:Any							=  i + 1
  return result


if __name__ == "__main__":
     print(solution())



