


import copy

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


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

__UpperCAmelCase : Tuple 			=				{
    "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'''
       __UpperCamelCase      :     List[Any]							=					"deformable_detr"
       __UpperCamelCase      :     str							=					{
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=300 ,					__SCREAMING_SNAKE_CASE=1_024 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=1_024 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=1_024 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="relu" ,					__SCREAMING_SNAKE_CASE=256 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1.0 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE="sine" ,					__SCREAMING_SNAKE_CASE="resnet50" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=300 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.25 ,					__SCREAMING_SNAKE_CASE=False ,					**__SCREAMING_SNAKE_CASE ,					):




              """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.'''						)
                            UpperCamelCase    :	List[Any]   	=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']						)
                     elif isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Any   	=		backbone_config.get('''model_type'''						)
                            UpperCamelCase    :	List[str]   	=		CONFIG_MAPPING[backbone_model_type]
                            UpperCamelCase    :	str   	=		config_class.from_dict(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		use_timm_backbone
              UpperCamelCase    :	Dict   	=		backbone_config
              UpperCamelCase    :	str   	=		num_channels
              UpperCamelCase    :	List[str]   	=		num_queries
              UpperCamelCase    :	int   	=		max_position_embeddings
              UpperCamelCase    :	str   	=		d_model
              UpperCamelCase    :	List[Any]   	=		encoder_ffn_dim
              UpperCamelCase    :	Tuple   	=		encoder_layers
              UpperCamelCase    :	List[str]   	=		encoder_attention_heads
              UpperCamelCase    :	List[str]   	=		decoder_ffn_dim
              UpperCamelCase    :	Optional[int]   	=		decoder_layers
              UpperCamelCase    :	int   	=		decoder_attention_heads
              UpperCamelCase    :	Tuple   	=		dropout
              UpperCamelCase    :	str   	=		attention_dropout
              UpperCamelCase    :	List[Any]   	=		activation_dropout
              UpperCamelCase    :	List[Any]   	=		activation_function
              UpperCamelCase    :	Tuple   	=		init_std
              UpperCamelCase    :	Optional[Any]   	=		init_xavier_std
              UpperCamelCase    :	Any   	=		encoder_layerdrop
              UpperCamelCase    :	List[Any]   	=		auxiliary_loss
              UpperCamelCase    :	Union[str, Any]   	=		position_embedding_type
              UpperCamelCase    :	Union[str, Any]   	=		backbone
              UpperCamelCase    :	List[str]   	=		use_pretrained_backbone
              UpperCamelCase    :	int   	=		dilation
              # deformable attributes
              UpperCamelCase    :	str   	=		num_feature_levels
              UpperCamelCase    :	Union[str, Any]   	=		encoder_n_points
              UpperCamelCase    :	Tuple   	=		decoder_n_points
              UpperCamelCase    :	Optional[Any]   	=		two_stage
              UpperCamelCase    :	Union[str, Any]   	=		two_stage_num_proposals
              UpperCamelCase    :	Tuple   	=		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
              UpperCamelCase    :	Dict   	=		class_cost
              UpperCamelCase    :	List[str]   	=		bbox_cost
              UpperCamelCase    :	Dict   	=		giou_cost
              # Loss coefficients
              UpperCamelCase    :	int   	=		mask_loss_coefficient
              UpperCamelCase    :	Tuple   	=		dice_loss_coefficient
              UpperCamelCase    :	List[Any]   	=		bbox_loss_coefficient
              UpperCamelCase    :	List[str]   	=		giou_loss_coefficient
              UpperCamelCase    :	Tuple   	=		eos_coefficient
              UpperCamelCase    :	Optional[int]   	=		focal_alpha
              UpperCamelCase    :	str   	=		disable_custom_kernels
              super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.encoder_attention_heads






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.d_model






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		copy.deepcopy(self.__dict__						)
              if self.backbone_config is not None:
                     UpperCamelCase    :	str   	=		self.backbone_config.to_dict()
              UpperCamelCase    :	List[str]   	=		self.__class__.model_type
              return output





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )

