





import copy

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


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """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        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       List[str]      					="""deformable_detr"""
  a_							:       int      					={
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }







  def __init__(      self      :					Optional[int]      ,				UpperCamelCase      :					Any=True      ,				UpperCamelCase      :					Union[str, Any]=None      ,				UpperCamelCase      :					List[Any]=3      ,				UpperCamelCase      :					Union[str, Any]=3_00      ,				UpperCamelCase      :					str=10_24      ,				UpperCamelCase      :					int=6      ,				UpperCamelCase      :					Optional[int]=10_24      ,				UpperCamelCase      :					List[str]=8      ,				UpperCamelCase      :					List[Any]=6      ,				UpperCamelCase      :					Union[str, Any]=10_24      ,				UpperCamelCase      :					Union[str, Any]=8      ,				UpperCamelCase      :					List[str]=0.0      ,				UpperCamelCase      :					str=True      ,				UpperCamelCase      :					str="relu"      ,				UpperCamelCase      :					Optional[Any]=2_56      ,				UpperCamelCase      :					str=0.1      ,				UpperCamelCase      :					Dict=0.0      ,				UpperCamelCase      :					List[Any]=0.0      ,				UpperCamelCase      :					Union[str, Any]=0.02      ,				UpperCamelCase      :					Optional[int]=1.0      ,				UpperCamelCase      :					str=True      ,				UpperCamelCase      :					List[str]=False      ,				UpperCamelCase      :					Any="sine"      ,				UpperCamelCase      :					Tuple="resnet50"      ,				UpperCamelCase      :					List[str]=True      ,				UpperCamelCase      :					List[Any]=False      ,				UpperCamelCase      :					Tuple=4      ,				UpperCamelCase      :					int=4      ,				UpperCamelCase      :					Optional[int]=4      ,				UpperCamelCase      :					List[Any]=False      ,				UpperCamelCase      :					Dict=3_00      ,				UpperCamelCase      :					Optional[Any]=False      ,				UpperCamelCase      :					Union[str, Any]=1      ,				UpperCamelCase      :					str=5      ,				UpperCamelCase      :					str=2      ,				UpperCamelCase      :					Optional[Any]=1      ,				UpperCamelCase      :					str=1      ,				UpperCamelCase      :					str=5      ,				UpperCamelCase      :					Optional[int]=2      ,				UpperCamelCase      :					int=0.1      ,				UpperCamelCase      :					Tuple=0.25      ,				UpperCamelCase      :					Any=False      ,				**UpperCamelCase      :					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.'							)
        _snake_case   :				Optional[int]														=	CONFIG_MAPPING['resnet'](out_features=['stage4']							)
      elif isinstance(UpperCamelCase      ,				UpperCamelCase							):
        _snake_case   :				Dict														=	backbone_config.get('model_type'							)
        _snake_case   :				Tuple														=	CONFIG_MAPPING[backbone_model_type]
        _snake_case   :				int														=	config_class.from_dict(UpperCamelCase							)
    _snake_case   :				Union[str, Any]														=	use_timm_backbone
    _snake_case   :				Dict														=	backbone_config
    _snake_case   :				Optional[Any]														=	num_channels
    _snake_case   :				Any														=	num_queries
    _snake_case   :				List[Any]														=	max_position_embeddings
    _snake_case   :				Dict														=	d_model
    _snake_case   :				Optional[int]														=	encoder_ffn_dim
    _snake_case   :				Dict														=	encoder_layers
    _snake_case   :				Any														=	encoder_attention_heads
    _snake_case   :				List[Any]														=	decoder_ffn_dim
    _snake_case   :				Optional[int]														=	decoder_layers
    _snake_case   :				Any														=	decoder_attention_heads
    _snake_case   :				Tuple														=	dropout
    _snake_case   :				List[Any]														=	attention_dropout
    _snake_case   :				Optional[Any]														=	activation_dropout
    _snake_case   :				Tuple														=	activation_function
    _snake_case   :				Any														=	init_std
    _snake_case   :				List[str]														=	init_xavier_std
    _snake_case   :				Dict														=	encoder_layerdrop
    _snake_case   :				List[str]														=	auxiliary_loss
    _snake_case   :				Union[str, Any]														=	position_embedding_type
    _snake_case   :				Optional[Any]														=	backbone
    _snake_case   :				List[str]														=	use_pretrained_backbone
    _snake_case   :				Dict														=	dilation
    # deformable attributes
    _snake_case   :				Optional[int]														=	num_feature_levels
    _snake_case   :				Optional[Any]														=	encoder_n_points
    _snake_case   :				Union[str, Any]														=	decoder_n_points
    _snake_case   :				List[str]														=	two_stage
    _snake_case   :				Dict														=	two_stage_num_proposals
    _snake_case   :				Optional[Any]														=	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   :				str														=	class_cost
    _snake_case   :				List[str]														=	bbox_cost
    _snake_case   :				str														=	giou_cost
    # Loss coefficients
    _snake_case   :				str														=	mask_loss_coefficient
    _snake_case   :				Tuple														=	dice_loss_coefficient
    _snake_case   :				str														=	bbox_loss_coefficient
    _snake_case   :				str														=	giou_loss_coefficient
    _snake_case   :				List[Any]														=	eos_coefficient
    _snake_case   :				List[str]														=	focal_alpha
    _snake_case   :				Dict														=	disable_custom_kernels
    super().__init__(is_encoder_decoder=UpperCamelCase      ,				**UpperCamelCase							)







  @property
  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    return self.encoder_attention_heads







  @property
  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    return self.d_model







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _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   :				List[str]														=	self.__class__.model_type
    return output











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		int:

  if not isinstance(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				Union[str, Any]														=	F"""Input value of [number={number}] must be an integer"""
    raise TypeError(lowerCAmelCase				)

  if number < 1:
    _snake_case   :				int														=	F"""Input value of [number={number}] must be > 0"""
    raise ValueError(lowerCAmelCase				)

  _snake_case   :				int														=	1

  for i in range(1    ,							lowerCAmelCase				):
    current_number *= 4 * i - 2
    current_number //= i + 1

  return current_number


if __name__ == "__main__":
      import doctest

      doctest.testmod()




