
"""simple docstring"""





import copy

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


_a														=		logging.get_logger(__name__)

_a														=		{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'deta'
		lowercase__      =				{
		    '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    ,		)	->	List[str]:






							'''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(__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 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.encoder_attention_heads



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.d_model




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






							'''simple docstring'''
							_UpperCamelCase										=  copy.deepcopy(self.__dict__)
							_UpperCamelCase										=  self.backbone_config.to_dict()
							_UpperCamelCase										=  self.__class__.model_type
							return output








"""simple docstring"""





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)






