



'''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


lowercase_									=    {
    "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
}

lowercase_									=    logging.get_logger(__name__)






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			'maskformer'
 __lowerCamelCase       :			int		=			{'hidden_size': 'mask_feature_size'}
 __lowerCamelCase       :			List[str]		=			['resnet', 'swin']
 __lowerCamelCase       :			Dict		=			['detr']



 def __init__(self					,  A = 256					,  A = 256					,  A = 0.1					,  A = False					,  A = None					,  A = None					,  A = 0.02					,  A = 1.0					,  A = 1.0					,  A = 1.0					,  A = 20.0					,  A = None					,  **A					,  )       ->	List[Any]:


      """simple docstring"""



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

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

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



 @classmethod
 def    a__    (cls					,  A					,  A					,  **A			)       ->	Optional[Any]:


      """simple docstring"""



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



 def    a__    (self			)       ->	Dict[str, any]:


      """simple docstring"""



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








'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     return len(set(__A)) == len(__A)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



