



'''simple docstring'''


import copy

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


lowercase              =			logging.get_logger(__name__)

lowercase              =			{
    '''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'''




 __magic_name__					: List[Any]  =      "deformable_detr"
 __magic_name__					: Any  =      {
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }




 def __init__(   self							,  lowerCAmelCase_=True							,  lowerCAmelCase_=None							,  lowerCAmelCase_=3							,  lowerCAmelCase_=3_0_0							,  lowerCAmelCase_=1_0_2_4							,  lowerCAmelCase_=6							,  lowerCAmelCase_=1_0_2_4							,  lowerCAmelCase_=8							,  lowerCAmelCase_=6							,  lowerCAmelCase_=1_0_2_4							,  lowerCAmelCase_=8							,  lowerCAmelCase_=0.0							,  lowerCAmelCase_=True							,  lowerCAmelCase_="relu"							,  lowerCAmelCase_=2_5_6							,  lowerCAmelCase_=0.1							,  lowerCAmelCase_=0.0							,  lowerCAmelCase_=0.0							,  lowerCAmelCase_=0.0_2							,  lowerCAmelCase_=1.0							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_="sine"							,  lowerCAmelCase_="resnet50"							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=4							,  lowerCAmelCase_=4							,  lowerCAmelCase_=4							,  lowerCAmelCase_=False							,  lowerCAmelCase_=3_0_0							,  lowerCAmelCase_=False							,  lowerCAmelCase_=1							,  lowerCAmelCase_=5							,  lowerCAmelCase_=2							,  lowerCAmelCase_=1							,  lowerCAmelCase_=1							,  lowerCAmelCase_=5							,  lowerCAmelCase_=2							,  lowerCAmelCase_=0.1							,  lowerCAmelCase_=0.2_5							,  lowerCAmelCase_=False							,  **lowerCAmelCase_							,  ) ->      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_       		=CONFIG_MAPPING["""resnet"""](out_features=["stage4"])
           elif isinstance(a_							,  a_):
                a_       		=backbone_config.get("model_type")
                a_       		=CONFIG_MAPPING[backbone_model_type]
                a_       		=config_class.from_dict(a_)
      a_       		=use_timm_backbone
      a_       		=backbone_config
      a_       		=num_channels
      a_       		=num_queries
      a_       		=max_position_embeddings
      a_       		=d_model
      a_       		=encoder_ffn_dim
      a_       		=encoder_layers
      a_       		=encoder_attention_heads
      a_       		=decoder_ffn_dim
      a_       		=decoder_layers
      a_       		=decoder_attention_heads
      a_       		=dropout
      a_       		=attention_dropout
      a_       		=activation_dropout
      a_       		=activation_function
      a_       		=init_std
      a_       		=init_xavier_std
      a_       		=encoder_layerdrop
      a_       		=auxiliary_loss
      a_       		=position_embedding_type
      a_       		=backbone
      a_       		=use_pretrained_backbone
      a_       		=dilation
      # deformable attributes
      a_       		=num_feature_levels
      a_       		=encoder_n_points
      a_       		=decoder_n_points
      a_       		=two_stage
      a_       		=two_stage_num_proposals
      a_       		=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
      a_       		=class_cost
      a_       		=bbox_cost
      a_       		=giou_cost
      # Loss coefficients
      a_       		=mask_loss_coefficient
      a_       		=dice_loss_coefficient
      a_       		=bbox_loss_coefficient
      a_       		=giou_loss_coefficient
      a_       		=eos_coefficient
      a_       		=focal_alpha
      a_       		=disable_custom_kernels
      super().__init__(is_encoder_decoder=a_							,  **a_)




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



      """simple docstring"""







      return self.encoder_attention_heads




 @property
 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      return self.d_model




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      a_       		=copy.deepcopy(self.__dict__)
      if self.backbone_config is not None:
           a_       		=self.backbone_config.to_dict()
      a_       		=self.__class__.model_type
      return output







'''simple docstring'''


from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip


