


import copy

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


_lowerCAmelCase:						Tuple   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Optional[Any]   				=       {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='deta'
				snake_case	    ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}





				def __init__(				self       ,  lowercase_=None       ,  lowercase_=900       ,  lowercase_=2048       ,  lowercase_=6       ,  lowercase_=2048       ,  lowercase_=8       ,  lowercase_=6       ,  lowercase_=1024       ,  lowercase_=8       ,  lowercase_=0.0       ,  lowercase_=True       ,  lowercase_="relu"       ,  lowercase_=256       ,  lowercase_=0.1       ,  lowercase_=0.0       ,  lowercase_=0.0       ,  lowercase_=0.02       ,  lowercase_=1.0       ,  lowercase_=True       ,  lowercase_=False       ,  lowercase_="sine"       ,  lowercase_=5       ,  lowercase_=4       ,  lowercase_=4       ,  lowercase_=True       ,  lowercase_=300       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=1       ,  lowercase_=5       ,  lowercase_=2       ,  lowercase_=1       ,  lowercase_=1       ,  lowercase_=5       ,  lowercase_=2       ,  lowercase_=0.1       ,  lowercase_=0.25       ,  **lowercase_       ,  )  -> int:
											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=['stage2', 'stage3', 'stage4'])
											else:
																		if isinstance(lowercase_       ,  lowercase_):
																									a__      =backbone_config.pop('model_type')
																									a__      =CONFIG_MAPPING[backbone_model_type]
																									a__      =config_class.from_dict(lowercase_)

											a__      =backbone_config
											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
											# 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
											a__      =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
											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
											super().__init__(is_encoder_decoder=lowercase_       ,  **lowercase_)





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return self.encoder_attention_heads





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return self.d_model





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =copy.deepcopy(self.__dict__)
											a__      =self.backbone_config.to_dict()
											a__      =self.__class__.model_type
											return output





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						List[Any]   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

