


'''simple docstring'''







import copy

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


a__  :					List[str]        =       logging.get_logger(__name__)

a__  :					Optional[int]        =       {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	'deta'
    __UpperCAmelCase   			=	{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }


    def __init__(     self  ,					a=None  ,					a=9_00  ,					a=20_48  ,					a=6  ,					a=20_48  ,					a=8  ,					a=6  ,					a=10_24  ,					a=8  ,					a=0.0  ,					a=True  ,					a="relu"  ,					a=2_56  ,					a=0.1  ,					a=0.0  ,					a=0.0  ,					a=0.02  ,					a=1.0  ,					a=True  ,					a=False  ,					a="sine"  ,					a=5  ,					a=4  ,					a=4  ,					a=True  ,					a=3_00  ,					a=True  ,					a=True  ,					a=1  ,					a=5  ,					a=2  ,					a=1  ,					a=1  ,					a=5  ,					a=2  ,					a=0.1  ,					a=0.25  ,					**a  ,					):
          if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."    )
                UpperCamelCase__										=				CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]    )
          else:
                if isinstance(a  ,					a    ):
                      UpperCamelCase__										=				backbone_config.pop("model_type"    )
                      UpperCamelCase__										=				CONFIG_MAPPING[backbone_model_type]
                      UpperCamelCase__										=				config_class.from_dict(a    )

          UpperCamelCase__										=				backbone_config
          UpperCamelCase__										=				num_queries
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				d_model
          UpperCamelCase__										=				encoder_ffn_dim
          UpperCamelCase__										=				encoder_layers
          UpperCamelCase__										=				encoder_attention_heads
          UpperCamelCase__										=				decoder_ffn_dim
          UpperCamelCase__										=				decoder_layers
          UpperCamelCase__										=				decoder_attention_heads
          UpperCamelCase__										=				dropout
          UpperCamelCase__										=				attention_dropout
          UpperCamelCase__										=				activation_dropout
          UpperCamelCase__										=				activation_function
          UpperCamelCase__										=				init_std
          UpperCamelCase__										=				init_xavier_std
          UpperCamelCase__										=				encoder_layerdrop
          UpperCamelCase__										=				auxiliary_loss
          UpperCamelCase__										=				position_embedding_type
          # deformable attributes
          UpperCamelCase__										=				num_feature_levels
          UpperCamelCase__										=				encoder_n_points
          UpperCamelCase__										=				decoder_n_points
          UpperCamelCase__										=				two_stage
          UpperCamelCase__										=				two_stage_num_proposals
          UpperCamelCase__										=				with_box_refine
          UpperCamelCase__										=				assign_first_stage
          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__										=				class_cost
          UpperCamelCase__										=				bbox_cost
          UpperCamelCase__										=				giou_cost
          # Loss coefficients
          UpperCamelCase__										=				mask_loss_coefficient
          UpperCamelCase__										=				dice_loss_coefficient
          UpperCamelCase__										=				bbox_loss_coefficient
          UpperCamelCase__										=				giou_loss_coefficient
          UpperCamelCase__										=				eos_coefficient
          UpperCamelCase__										=				focal_alpha
          super().__init__(is_encoder_decoder=a  ,					**a    )


    @property
    def    __a							(     self    ):
          return self.encoder_attention_heads


    @property
    def    __a							(     self    ):
          return self.d_model


    def    __a							(     self    ):
          UpperCamelCase__										=				copy.deepcopy(self.__dict__    )
          UpperCamelCase__										=				self.backbone_config.to_dict()
          UpperCamelCase__										=				self.__class__.model_type
          return output









"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline





