'''simple docstring'''

import copy

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


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

_A     : Dict    =							{
    '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 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE	:			int       =   """deformable_detr"""
 _SCREAMING_SNAKE_CASE	:			Optional[Any]       =   {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__( self  :      Union[str, Any] ,			SCREAMING_SNAKE_CASE__  :      Optional[Any]=True ,			SCREAMING_SNAKE_CASE__  :      Tuple=None ,			SCREAMING_SNAKE_CASE__  :      str=3 ,			SCREAMING_SNAKE_CASE__  :      List[str]=3_00 ,			SCREAMING_SNAKE_CASE__  :      List[Any]=10_24 ,			SCREAMING_SNAKE_CASE__  :      int=6 ,			SCREAMING_SNAKE_CASE__  :      List[str]=10_24 ,			SCREAMING_SNAKE_CASE__  :      Tuple=8 ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any]=6 ,			SCREAMING_SNAKE_CASE__  :      List[str]=10_24 ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any]=8 ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any]=0.0 ,			SCREAMING_SNAKE_CASE__  :      Any=True ,			SCREAMING_SNAKE_CASE__  :      List[str]="relu" ,			SCREAMING_SNAKE_CASE__  :      List[Any]=2_56 ,			SCREAMING_SNAKE_CASE__  :      Dict=0.1 ,			SCREAMING_SNAKE_CASE__  :      Tuple=0.0 ,			SCREAMING_SNAKE_CASE__  :      Optional[Any]=0.0 ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any]=0.0_2 ,			SCREAMING_SNAKE_CASE__  :      List[str]=1.0 ,			SCREAMING_SNAKE_CASE__  :      str=True ,			SCREAMING_SNAKE_CASE__  :      Tuple=False ,			SCREAMING_SNAKE_CASE__  :      Optional[Any]="sine" ,			SCREAMING_SNAKE_CASE__  :      Dict="resnet50" ,			SCREAMING_SNAKE_CASE__  :      Optional[int]=True ,			SCREAMING_SNAKE_CASE__  :      List[Any]=False ,			SCREAMING_SNAKE_CASE__  :      List[Any]=4 ,			SCREAMING_SNAKE_CASE__  :      List[Any]=4 ,			SCREAMING_SNAKE_CASE__  :      Dict=4 ,			SCREAMING_SNAKE_CASE__  :      Any=False ,			SCREAMING_SNAKE_CASE__  :      List[Any]=3_00 ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any]=False ,			SCREAMING_SNAKE_CASE__  :      Optional[Any]=1 ,			SCREAMING_SNAKE_CASE__  :      Dict=5 ,			SCREAMING_SNAKE_CASE__  :      List[str]=2 ,			SCREAMING_SNAKE_CASE__  :      List[Any]=1 ,			SCREAMING_SNAKE_CASE__  :      Tuple=1 ,			SCREAMING_SNAKE_CASE__  :      List[str]=5 ,			SCREAMING_SNAKE_CASE__  :      List[str]=2 ,			SCREAMING_SNAKE_CASE__  :      str=0.1 ,			SCREAMING_SNAKE_CASE__  :      List[Any]=0.2_5 ,			SCREAMING_SNAKE_CASE__  :      Tuple=False ,			**SCREAMING_SNAKE_CASE__  :      Tuple ,			)    ->       Tuple:
  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											=		CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]				)
   elif isinstance(A_ ,			A_				):
    __lowerCAmelCase											=		backbone_config.get("""model_type"""				)
    __lowerCAmelCase											=		CONFIG_MAPPING[backbone_model_type]
    __lowerCAmelCase											=		config_class.from_dict(A_				)
  __lowerCAmelCase											=		use_timm_backbone
  __lowerCAmelCase											=		backbone_config
  __lowerCAmelCase											=		num_channels
  __lowerCAmelCase											=		num_queries
  __lowerCAmelCase											=		max_position_embeddings
  __lowerCAmelCase											=		d_model
  __lowerCAmelCase											=		encoder_ffn_dim
  __lowerCAmelCase											=		encoder_layers
  __lowerCAmelCase											=		encoder_attention_heads
  __lowerCAmelCase											=		decoder_ffn_dim
  __lowerCAmelCase											=		decoder_layers
  __lowerCAmelCase											=		decoder_attention_heads
  __lowerCAmelCase											=		dropout
  __lowerCAmelCase											=		attention_dropout
  __lowerCAmelCase											=		activation_dropout
  __lowerCAmelCase											=		activation_function
  __lowerCAmelCase											=		init_std
  __lowerCAmelCase											=		init_xavier_std
  __lowerCAmelCase											=		encoder_layerdrop
  __lowerCAmelCase											=		auxiliary_loss
  __lowerCAmelCase											=		position_embedding_type
  __lowerCAmelCase											=		backbone
  __lowerCAmelCase											=		use_pretrained_backbone
  __lowerCAmelCase											=		dilation
  # deformable attributes
  __lowerCAmelCase											=		num_feature_levels
  __lowerCAmelCase											=		encoder_n_points
  __lowerCAmelCase											=		decoder_n_points
  __lowerCAmelCase											=		two_stage
  __lowerCAmelCase											=		two_stage_num_proposals
  __lowerCAmelCase											=		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
  __lowerCAmelCase											=		class_cost
  __lowerCAmelCase											=		bbox_cost
  __lowerCAmelCase											=		giou_cost
  # Loss coefficients
  __lowerCAmelCase											=		mask_loss_coefficient
  __lowerCAmelCase											=		dice_loss_coefficient
  __lowerCAmelCase											=		bbox_loss_coefficient
  __lowerCAmelCase											=		giou_loss_coefficient
  __lowerCAmelCase											=		eos_coefficient
  __lowerCAmelCase											=		focal_alpha
  __lowerCAmelCase											=		disable_custom_kernels
  super().__init__(is_encoder_decoder=A_ ,			**A_				)
 @property
 def a   ( self  :      Union[str, Any]				)    ->       Optional[int]:
  return self.encoder_attention_heads
 @property
 def a   ( self  :      Any				)    ->       int:
  return self.d_model





 def a   ( self  :      Any				)    ->       List[str]:
  __lowerCAmelCase											=		copy.deepcopy(self.__dict__				)
  if self.backbone_config is not None:
   __lowerCAmelCase											=		self.backbone_config.to_dict()
  __lowerCAmelCase											=		self.__class__.model_type
  return output










import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=A     )
class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    field(default='''image-classification'''							,	metadata={'''include_in_asdict_even_if_is_default''': True}     )
     __SCREAMING_SNAKE_CASE     					=    Features({'''image''': Image()}     )
     __SCREAMING_SNAKE_CASE     					=    Features({'''labels''': ClassLabel}     )
     __SCREAMING_SNAKE_CASE     					=    "image"
     __SCREAMING_SNAKE_CASE     					=    "labels"


     def        __snake_case(  self							,					A_    ):
       if self.label_column not in features:
         raise ValueError(F'''Column {self.label_column} is not present in features.'''    )
       if not isinstance(features[self.label_column]							,					A_    ):
         raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''    )
       _UpperCAmelCase		:							Tuple							=     copy.deepcopy(self    )
       _UpperCAmelCase		:							str							=     self.label_schema.copy()
       _UpperCAmelCase		:							Optional[Any]							=     features[self.label_column]
       _UpperCAmelCase		:							int							=     label_schema
       return task_template




     @property
     def        __snake_case(  self    ):
       return {
           self.image_column: "image",
           self.label_column: "labels",
       }





