






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	:							Tuple				={
    '''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__       ):
 _lowerCAmelCase       :       Optional[int]        = '''maskformer'''
 _lowerCAmelCase       :       Tuple        = {'''hidden_size''': '''mask_feature_size'''}
 _lowerCAmelCase       :       int        = ['''resnet''', '''swin''']
 _lowerCAmelCase       :       Any        = ['''detr''']







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   int = 2_56							,   SCREAMING_SNAKE_CASE					:   int = 2_56							,   SCREAMING_SNAKE_CASE					:   float = 0.1							,   SCREAMING_SNAKE_CASE					:   bool = False							,   SCREAMING_SNAKE_CASE					:   Optional[Dict] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Dict] = None							,   SCREAMING_SNAKE_CASE					:   float = 0.0_2							,   SCREAMING_SNAKE_CASE					:   float = 1.0							,   SCREAMING_SNAKE_CASE					:   float = 1.0							,   SCREAMING_SNAKE_CASE					:   float = 1.0							,   SCREAMING_SNAKE_CASE					:   float = 2_0.0							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   **SCREAMING_SNAKE_CASE					:   List[Any]							,   ):


      '''simple docstring'''






      if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           UpperCamelCase__			:   List[str]       				=  SwinConfig(
               image_size=3_84							,   in_channels=3							,   patch_size=4							,   embed_dim=1_28							,   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(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Dict       				=  backbone_config.pop("model_type"						)
           UpperCamelCase__			:   List[str]       				=  CONFIG_MAPPING[backbone_model_type]
           UpperCamelCase__			:   Dict       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)

      # 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
           UpperCamelCase__			:   int       				=  DetrConfig()
      else:
           # verify that the decoder is supported
           UpperCamelCase__			:   Dict       				=  (
               decoder_config.pop("model_type"						) if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) 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(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   List[Any]       				=  CONFIG_MAPPING[decoder_type]
                UpperCamelCase__			:   str       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)

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

      UpperCamelCase__			:   int       				=  self.decoder_config.encoder_attention_heads
      UpperCamelCase__			:   str       				=  self.decoder_config.num_hidden_layers
      super().__init__(**SCREAMING_SNAKE_CASE						)







 @classmethod
 def 					__lowercase					(		cls					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   PretrainedConfig							,   SCREAMING_SNAKE_CASE					:   PretrainedConfig							,   **SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






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







import numpy as np







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return 1 / (1 + np.exp(-vector			))







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return vector * sigmoid(1.7_0_2 * vector			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()