"""simple docstring"""




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_a				:		Union[str, Any]	   =       {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_a				:		Tuple	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_		):
					_UpperCamelCase							:      List[Any]		 =		"maskformer"
					_UpperCamelCase							:      Union[str, Any]		 =		{"hidden_size": "mask_feature_size"}
					_UpperCamelCase							:      str		 =		["resnet", "swin"]
					_UpperCamelCase							:      int		 =		["detr"]






					def __init__(		self    ,					a__ = 256    ,					a__ = 256    ,					a__ = 0.1    ,					a__ = False    ,					a__ = None    ,					a__ = None    ,					a__ = 0.0_2    ,					a__ = 1.0    ,					a__ = 1.0    ,					a__ = 1.0    ,					a__ = 20.0    ,					a__ = None    ,					**a__    ,					):
							if backbone_config is None:
									# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
									_lowerCAmelCase						:	Dict        =					SwinConfig(
									    image_size=384    ,					in_channels=3    ,					patch_size=4    ,					embed_dim=128    ,					depths=[2, 2, 18, 2]    ,					num_heads=[4, 8, 16, 32]    ,					window_size=12    ,					drop_path_rate=0.3    ,					out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,					)

							if isinstance(a__    ,					a__  ):
									_lowerCAmelCase						:	Optional[Any]        =					backbone_config.pop("""model_type"""  )
									_lowerCAmelCase						:	List[Any]        =					CONFIG_MAPPING[backbone_model_type]
									_lowerCAmelCase						:	str        =					config_class.from_dict(a__  )

							# verify that the backbone is supported
							if backbone_config.model_type not in self.backbones_supported:
									logger.warning_once(
									    F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
									    F"Supported model types: {','.join(self.backbones_supported  )}"  )

							if decoder_config is None:
									# fall back to https://huggingface.co/facebook/detr-resnet-50
									_lowerCAmelCase						:	str        =					DetrConfig()
							else:
									# verify that the decoder is supported
									_lowerCAmelCase						:	Optional[Any]        =					(
									    decoder_config.pop("""model_type"""  ) if isinstance(a__    ,					a__  ) else decoder_config.model_type
									)
									if decoder_type not in self.decoders_supported:
											raise ValueError(
											    F"Transformer Decoder {decoder_type} not supported, please use one of"
											    F" {','.join(self.decoders_supported  )}"  )
									if isinstance(a__    ,					a__  ):
											_lowerCAmelCase						:	List[Any]        =					CONFIG_MAPPING[decoder_type]
											_lowerCAmelCase						:	Optional[Any]        =					config_class.from_dict(a__  )

							_lowerCAmelCase						:	Tuple        =					backbone_config
							_lowerCAmelCase						:	str        =					decoder_config
							# main feature dimension for the model
							_lowerCAmelCase						:	List[Any]        =					fpn_feature_size
							_lowerCAmelCase						:	Tuple        =					mask_feature_size
							# initializer
							_lowerCAmelCase						:	int        =					init_std
							_lowerCAmelCase						:	Union[str, Any]        =					init_xavier_std
							# Hungarian matcher && loss
							_lowerCAmelCase						:	int        =					cross_entropy_weight
							_lowerCAmelCase						:	Dict        =					dice_weight
							_lowerCAmelCase						:	Optional[Any]        =					mask_weight
							_lowerCAmelCase						:	Dict        =					use_auxiliary_loss
							_lowerCAmelCase						:	Union[str, Any]        =					no_object_weight
							_lowerCAmelCase						:	Optional[int]        =					output_auxiliary_logits

							_lowerCAmelCase						:	int        =					self.decoder_config.encoder_attention_heads
							_lowerCAmelCase						:	Tuple        =					self.decoder_config.num_hidden_layers
							super().__init__(**a__  )






					@classmethod
					def    __A  (		cls    ,					a__    ,					a__    ,					**a__  ):
							return cls(
							    backbone_config=a__    ,					decoder_config=a__    ,					**a__    ,					)






					def    __A  (		self  ):
							_lowerCAmelCase						:	Dict        =					copy.deepcopy(self.__dict__  )
							_lowerCAmelCase						:	Optional[Any]        =					self.backbone_config.to_dict()
							_lowerCAmelCase						:	List[Any]        =					self.decoder_config.to_dict()
							_lowerCAmelCase						:	int        =					self.__class__.model_type
							return output





"""simple docstring"""




from __future__ import annotations





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	list     )	->				float:
  if not nums:
    raise ValueError("""List is empty"""     )
  return sum(_lowerCamelCase     ) / len(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




