


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


lowerCamelCase		   =			{
    '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
}

lowerCamelCase		   =			logging.get_logger(__name__)




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] ='maskformer'
      UpperCamelCase__				:    Dict ={'hidden_size': 'mask_feature_size'}
      UpperCamelCase__				:    Union[str, Any] =['resnet', 'swin']
      UpperCamelCase__				:    Any =['detr']






      def __init__(   self		:	Tuple     ,  lowercase_		:	int = 256     ,  lowercase_		:	int = 256     ,  lowercase_		:	float = 0.1     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[Dict] = None     ,  lowercase_		:	Optional[Dict] = None     ,  lowercase_		:	float = 0.02     ,  lowercase_		:	float = 1.0     ,  lowercase_		:	float = 1.0     ,  lowercase_		:	float = 1.0     ,  lowercase_		:	float = 20.0     ,  lowercase_		:	Optional[bool] = None     ,  **lowercase_		:	Dict     ,  )   ->		Dict:
             """simple docstring"""




             if backbone_config is None:
                    # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
                    _lowerCamelCase			:						Optional[int]            =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(lowercase_     ,  lowercase_					):
                    _lowerCamelCase			:						str            =backbone_config.pop('model_type'					)
                    _lowerCamelCase			:						int            =CONFIG_MAPPING[backbone_model_type]
                    _lowerCamelCase			:						str            =config_class.from_dict(lowercase_					)

             # 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			:						Union[str, Any]            =DetrConfig()
             else:
                    # verify that the decoder is supported
                    _lowerCamelCase			:						List[Any]            =(
                        decoder_config.pop('model_type'					) if isinstance(lowercase_     ,  lowercase_					) 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(lowercase_     ,  lowercase_					):
                           _lowerCamelCase			:						List[Any]            =CONFIG_MAPPING[decoder_type]
                           _lowerCamelCase			:						List[str]            =config_class.from_dict(lowercase_					)

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

             _lowerCamelCase			:						List[Any]            =self.decoder_config.encoder_attention_heads
             _lowerCamelCase			:						Dict            =self.decoder_config.num_hidden_layers
             super().__init__(**lowercase_					)






      @classmethod
      def 			lowerCamelCase			(   cls		:	List[str]     ,  lowercase_		:	PretrainedConfig     ,  lowercase_		:	PretrainedConfig     ,  **lowercase_		:	int					)   ->		Any:
             """simple docstring"""




             return cls(
                 backbone_config=lowercase_     ,  decoder_config=lowercase_     ,  **lowercase_     ,  )






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Dict[str, any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =copy.deepcopy(self.__dict__					)
             _lowerCamelCase			:						List[str]            =self.backbone_config.to_dict()
             _lowerCamelCase			:						Tuple            =self.decoder_config.to_dict()
             _lowerCamelCase			:						Optional[int]            =self.__class__.model_type
             return output






def       a_ (       SCREAMING_SNAKE_CASE__   : bytes	):
       '''simple docstring'''





       return "".join([hex(SCREAMING_SNAKE_CASE__	)[2:].zfill(2	).upper() for byte in list(SCREAMING_SNAKE_CASE__	)]	)




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       if (len(SCREAMING_SNAKE_CASE__	) % 2) != 0:
              raise ValueError(
                  'Base16 encoded data is invalid:\nData does not have an even number of hex digits.'	)
       # Check the character set - the standard base16 alphabet
       # is uppercase according to RFC3548 section 6
       if not set(SCREAMING_SNAKE_CASE__	) <= set('0123456789ABCDEF'	):
              raise ValueError(
                  'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.'	)
       # For every two hexadecimal digits (= a byte), turn it into an integer.
       # Then, string the result together into bytes, and return it.
       return bytes(int(data[i] + data[i + 1]						,     16	) for i in range(0						,     len(SCREAMING_SNAKE_CASE__	)						,     2	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


