





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__   : Union[str, Any]					  = {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Optional[int]			    =       'deta'
					lowerCamelCase     :     Tuple			    =       {
					    'hidden_size': 'd_model',
					    'num_attention_heads': 'encoder_attention_heads',
					}





					def __init__(						self    :  Dict ,					__lowercase    :  Union[str, Any]=None ,					__lowercase    :  Any=9_00 ,					__lowercase    :  Optional[Any]=20_48 ,					__lowercase    :  str=6 ,					__lowercase    :  Tuple=20_48 ,					__lowercase    :  Optional[Any]=8 ,					__lowercase    :  Optional[Any]=6 ,					__lowercase    :  Any=10_24 ,					__lowercase    :  str=8 ,					__lowercase    :  Optional[Any]=0.0 ,					__lowercase    :  Tuple=True ,					__lowercase    :  Optional[int]="relu" ,					__lowercase    :  Dict=2_56 ,					__lowercase    :  int=0.1 ,					__lowercase    :  Optional[Any]=0.0 ,					__lowercase    :  List[Any]=0.0 ,					__lowercase    :  List[str]=0.02 ,					__lowercase    :  Optional[Any]=1.0 ,					__lowercase    :  Union[str, Any]=True ,					__lowercase    :  Dict=False ,					__lowercase    :  Dict="sine" ,					__lowercase    :  Optional[Any]=5 ,					__lowercase    :  Dict=4 ,					__lowercase    :  Optional[int]=4 ,					__lowercase    :  List[Any]=True ,					__lowercase    :  str=3_00 ,					__lowercase    :  Optional[int]=True ,					__lowercase    :  Any=True ,					__lowercase    :  Union[str, Any]=1 ,					__lowercase    :  Dict=5 ,					__lowercase    :  List[str]=2 ,					__lowercase    :  List[str]=1 ,					__lowercase    :  Optional[Any]=1 ,					__lowercase    :  Dict=5 ,					__lowercase    :  str=2 ,					__lowercase    :  Tuple=0.1 ,					__lowercase    :  Optional[Any]=0.25 ,					**__lowercase    :  Union[str, Any] ,					):






						'''simple docstring'''







						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(__lowercase ,					__lowercase		):
								UpperCAmelCase_								=     backbone_config.pop("""model_type"""		)
								UpperCAmelCase_								=     CONFIG_MAPPING[backbone_model_type]
								UpperCAmelCase_								=     config_class.from_dict(__lowercase		)

						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=__lowercase ,					**__lowercase		)





					@property
					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						return self.encoder_attention_heads





					@property
					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						return self.d_model





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     copy.deepcopy(self.__dict__		)
						UpperCAmelCase_								=     self.backbone_config.to_dict()
						UpperCAmelCase_								=     self.__class__.model_type
						return output









import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def    A_(			A				,      A				,      **A   ):
	UpperCAmelCase_								=     AutoConfig.from_pretrained(A				,      **A   )
	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_config(A   )
	model.save_pretrained(A   )
	AutoTokenizer.from_pretrained(A   ).save_pretrained(A   )
	return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)


