





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


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							:      Tuple         =    {
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					"""conditional_detr"""
   _UpperCamelCase			:    int  		=					["""past_key_values"""]
   _UpperCamelCase			:    Optional[Any]  		=					{
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }





   def __init__(			self       ,      snake_case=True       ,      snake_case=None       ,      snake_case=3       ,      snake_case=300       ,      snake_case=6       ,      snake_case=2_048       ,      snake_case=8       ,      snake_case=6       ,      snake_case=2_048       ,      snake_case=8       ,      snake_case=0.0       ,      snake_case=0.0       ,      snake_case=True       ,      snake_case="relu"       ,      snake_case=256       ,      snake_case=0.1       ,      snake_case=0.0       ,      snake_case=0.0       ,      snake_case=0.02       ,      snake_case=1.0       ,      snake_case=False       ,      snake_case="sine"       ,      snake_case="resnet50"       ,      snake_case=True       ,      snake_case=False       ,      snake_case=2       ,      snake_case=5       ,      snake_case=2       ,      snake_case=1       ,      snake_case=1       ,      snake_case=2       ,      snake_case=5       ,      snake_case=2       ,      snake_case=0.25       ,      **snake_case       ,      )					->	Optional[int]:
      """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__						:    Tuple   		=	CONFIG_MAPPING["resnet"](out_features=["stage4"]   )
         elif isinstance(snake_case       ,      snake_case   ):
            a__						:    int   		=	backbone_config.get("model_type"   )
            a__						:    Dict   		=	CONFIG_MAPPING[backbone_model_type]
            a__						:    str   		=	config_class.from_dict(snake_case   )

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





   @property
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      return self.encoder_attention_heads





   @property
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      return self.d_model





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    Dict   		=	copy.deepcopy(self.__dict__   )
      if self.backbone_config is not None:
         a__						:    int   		=	self.backbone_config.to_dict()
      a__						:    Tuple   		=	self.__class__.model_type
      return output







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					version.parse("""1.11"""	)





   @property
   def 						_snake_case			(			self   )					->	Mapping[str, Mapping[int, str]]:
      """simple docstring"""





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





   @property
   def 						_snake_case			(			self   )					->	float:
      """simple docstring"""





      return 1E-5





   @property
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      return 12








from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
  import accelerate






def    _A	(   lowerCamelCase ):
   if not is_accelerate_available():
      return method
   a__						:    List[Any]   		=	version.parse(accelerate.__version__ ).base_version
   if version.parse(lowerCamelCase ) < version.parse("0.17.0" ):
      return method

   def wrapper(self				,			*lowerCamelCase				,			**lowerCamelCase ):
      if hasattr(self				,			"_hf_hook" ) and hasattr(self._hf_hook				,			"pre_forward" ):
         self._hf_hook.pre_forward(self )
      return method(self				,			*lowerCamelCase				,			**lowerCamelCase )

   return wrapper

