

'''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


lowerCamelCase			:				Optional[int] 			=					{
    "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			:				int 			=					logging.get_logger(__name__)







class 							A__   (	A__  ):
  A__					=    'maskformer'
  A__					=    {'hidden_size': 'mask_feature_size'}
  A__					=    ['resnet', 'swin']
  A__					=    ['detr']





  def __init__(		self				:     Tuple							, _a				:     int = 256							, _a				:     int = 256							, _a				:     float = 0.1							, _a				:     bool = False							, _a				:     Optional[Dict] = None							, _a				:     Optional[Dict] = None							, _a				:     float = 0.02							, _a				:     float = 1.0							, _a				:     float = 1.0							, _a				:     float = 1.0							, _a				:     float = 20.0							, _a				:     Optional[bool] = None							, **_a				:     List[Any]							, )     ->   Dict:





       '''simple docstring'''






       if backbone_config is None:
            # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
            _SCREAMING_SNAKE_CASE							   =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			):
            _SCREAMING_SNAKE_CASE							   =backbone_config.pop('model_type'			)
            _SCREAMING_SNAKE_CASE							   =CONFIG_MAPPING[backbone_model_type]
            _SCREAMING_SNAKE_CASE							   =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
            _SCREAMING_SNAKE_CASE							   =DetrConfig()
       else:
            # verify that the decoder is supported
            _SCREAMING_SNAKE_CASE							   =(
                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			):
                 _SCREAMING_SNAKE_CASE							   =CONFIG_MAPPING[decoder_type]
                 _SCREAMING_SNAKE_CASE							   =config_class.from_dict(_a			)

       _SCREAMING_SNAKE_CASE							   =backbone_config
       _SCREAMING_SNAKE_CASE							   =decoder_config
       # main feature dimension for the model
       _SCREAMING_SNAKE_CASE							   =fpn_feature_size
       _SCREAMING_SNAKE_CASE							   =mask_feature_size
       # initializer
       _SCREAMING_SNAKE_CASE							   =init_std
       _SCREAMING_SNAKE_CASE							   =init_xavier_std
       # Hungarian matcher && loss
       _SCREAMING_SNAKE_CASE							   =cross_entropy_weight
       _SCREAMING_SNAKE_CASE							   =dice_weight
       _SCREAMING_SNAKE_CASE							   =mask_weight
       _SCREAMING_SNAKE_CASE							   =use_auxiliary_loss
       _SCREAMING_SNAKE_CASE							   =no_object_weight
       _SCREAMING_SNAKE_CASE							   =output_auxiliary_logits

       _SCREAMING_SNAKE_CASE							   =self.decoder_config.encoder_attention_heads
       _SCREAMING_SNAKE_CASE							   =self.decoder_config.num_hidden_layers
       super().__init__(**_a			)





  @classmethod
  def 						A  (		cls				:     str							, _a				:     PretrainedConfig							, _a				:     PretrainedConfig							, **_a				:     List[str]			)     ->   Any:





       '''simple docstring'''






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





  def 						A  (		self				:     Optional[int]			)     ->   Dict[str, any]:





       '''simple docstring'''






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



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					float							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if mass < 0:
          raise ValueError('The mass of a body cannot be negative'    )
     return 0.5 * mass * abs(_UpperCamelCase    ) * abs(_UpperCamelCase    )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
