






import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_a    :   List[str]        =			{
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_a    :   List[str]        =			logging.get_logger(__name__)







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''mask2former'''
      A          =    ['''swin''']
      A          =    {'''hidden_size''': '''hidden_dim'''}
      def __init__(   self    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 256    ,	_lowerCAmelCase = 256    ,	_lowerCAmelCase = 256    ,	_lowerCAmelCase = 1_024    ,	_lowerCAmelCase = "relu"    ,	_lowerCAmelCase = 6    ,	_lowerCAmelCase = 10    ,	_lowerCAmelCase = 8    ,	_lowerCAmelCase = 0.0    ,	_lowerCAmelCase = 2_048    ,	_lowerCAmelCase = False    ,	_lowerCAmelCase = False    ,	_lowerCAmelCase = 4    ,	_lowerCAmelCase = 255    ,	_lowerCAmelCase = 100    ,	_lowerCAmelCase = 0.1    ,	_lowerCAmelCase = 2.0    ,	_lowerCAmelCase = 5.0    ,	_lowerCAmelCase = 5.0    ,	_lowerCAmelCase = 12_544    ,	_lowerCAmelCase = 3.0    ,	_lowerCAmelCase = 0.75    ,	_lowerCAmelCase = 0.02    ,	_lowerCAmelCase = 1.0    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = [4, 8, 16, 32]    ,	_lowerCAmelCase = None    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          if backbone_config is None:
              logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."		)
              lowerCAmelCase__   :Union[str, Any]      							=				CONFIG_MAPPING["swin"](
                  image_size=224    ,	in_channels=3    ,	patch_size=4    ,	embed_dim=96    ,	depths=[2, 2, 18, 2]    ,	num_heads=[3, 6, 12, 24]    ,	window_size=7    ,	drop_path_rate=0.3    ,	use_absolute_embeddings=_lowerCAmelCase    ,	out_features=["stage1", "stage2", "stage3", "stage4"]    ,	)

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

          # 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 Mask2Former. '''
                  f'''Supported model types: {",".join(self.backbones_supported		)}'''		)

          lowerCAmelCase__   :int      							=				backbone_config
          lowerCAmelCase__   :List[str]      							=				feature_size
          lowerCAmelCase__   :Dict      							=				mask_feature_size
          lowerCAmelCase__   :str      							=				hidden_dim
          lowerCAmelCase__   :Tuple      							=				encoder_feedforward_dim
          lowerCAmelCase__   :Optional[int]      							=				activation_function
          lowerCAmelCase__   :List[str]      							=				encoder_layers
          lowerCAmelCase__   :List[Any]      							=				decoder_layers
          lowerCAmelCase__   :Optional[int]      							=				num_attention_heads
          lowerCAmelCase__   :Any      							=				dropout
          lowerCAmelCase__   :List[Any]      							=				dim_feedforward
          lowerCAmelCase__   :Tuple      							=				pre_norm
          lowerCAmelCase__   :Optional[int]      							=				enforce_input_projection
          lowerCAmelCase__   :Tuple      							=				common_stride
          lowerCAmelCase__   :Any      							=				ignore_value
          lowerCAmelCase__   :List[str]      							=				num_queries
          lowerCAmelCase__   :Tuple      							=				no_object_weight
          lowerCAmelCase__   :Optional[Any]      							=				class_weight
          lowerCAmelCase__   :Tuple      							=				mask_weight
          lowerCAmelCase__   :Union[str, Any]      							=				dice_weight
          lowerCAmelCase__   :Any      							=				train_num_points
          lowerCAmelCase__   :Optional[int]      							=				oversample_ratio
          lowerCAmelCase__   :Optional[int]      							=				importance_sample_ratio
          lowerCAmelCase__   :Any      							=				init_std
          lowerCAmelCase__   :Any      							=				init_xavier_std
          lowerCAmelCase__   :Tuple      							=				use_auxiliary_loss
          lowerCAmelCase__   :Any      							=				feature_strides
          lowerCAmelCase__   :Tuple      							=				output_auxiliary_logits
          lowerCAmelCase__   :Optional[Any]      							=				decoder_layers

          super().__init__(**_lowerCAmelCase		)
      @classmethod
      def 			snake_case_      (   cls    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          return cls(
              backbone_config=_lowerCAmelCase    ,	**_lowerCAmelCase    ,	)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				copy.deepcopy(self.__dict__		)
          lowerCAmelCase__   :Optional[Any]      							=				self.backbone_config.to_dict()
          lowerCAmelCase__   :Optional[int]      							=				self.__class__.model_type
          return output











def 				snake_case__							(							UpperCAmelCase							:  str       ):
    return "".join(chr(ord(UpperCAmelCase       ) - 3_2       ) if "a" <= char <= "z" else char for char in word       )


if __name__ == "__main__":
 from doctest import testmod

 testmod()



