






import copy
from typing import Dict, List, Optional

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


__UpperCamelCase					:		int	  =						{
    """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
}

__UpperCamelCase					:		Dict	  =						logging.get_logger(__name__)


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "mask2former"
							_UpperCAmelCase     =       ["swin"]
							_UpperCAmelCase     =       {"hidden_size": "hidden_dim"}
							def __init__(     self:      Optional[int]       ,	UpperCamelCase:      Optional[Dict] = None       ,	UpperCamelCase:      int = 2_56       ,	UpperCamelCase:      int = 2_56       ,	UpperCamelCase:      int = 2_56       ,	UpperCamelCase:      int = 10_24       ,	UpperCamelCase:      str = "relu"       ,	UpperCamelCase:      int = 6       ,	UpperCamelCase:      int = 10       ,	UpperCamelCase:      int = 8       ,	UpperCamelCase:      float = 0.0       ,	UpperCamelCase:      int = 20_48       ,	UpperCamelCase:      bool = False       ,	UpperCamelCase:      bool = False       ,	UpperCamelCase:      int = 4       ,	UpperCamelCase:      int = 2_55       ,	UpperCamelCase:      int = 1_00       ,	UpperCamelCase:      float = 0.1       ,	UpperCamelCase:      float = 2.0       ,	UpperCamelCase:      float = 5.0       ,	UpperCamelCase:      float = 5.0       ,	UpperCamelCase:      int = 1_25_44       ,	UpperCamelCase:      float = 3.0       ,	UpperCamelCase:      float = 0.75       ,	UpperCamelCase:      float = 0.02       ,	UpperCamelCase:      float = 1.0       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      List[int] = [4, 8, 16, 32]       ,	UpperCamelCase:      bool = None       ,	**UpperCamelCase:      List[str]       ,	)		->		Dict:
											if backbone_config is None:
															logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'						)
															snake_case__									=     CONFIG_MAPPING['swin'](
															    image_size=2_24       ,	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=UpperCamelCase       ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,	)

											if isinstance(UpperCamelCase       ,	UpperCamelCase						):
															snake_case__									=     backbone_config.pop('model_type'						)
															snake_case__									=     CONFIG_MAPPING[backbone_model_type]
															snake_case__									=     config_class.from_dict(UpperCamelCase						)

											# 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						)}'''						)

											snake_case__									=     backbone_config
											snake_case__									=     feature_size
											snake_case__									=     mask_feature_size
											snake_case__									=     hidden_dim
											snake_case__									=     encoder_feedforward_dim
											snake_case__									=     activation_function
											snake_case__									=     encoder_layers
											snake_case__									=     decoder_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     dropout
											snake_case__									=     dim_feedforward
											snake_case__									=     pre_norm
											snake_case__									=     enforce_input_projection
											snake_case__									=     common_stride
											snake_case__									=     ignore_value
											snake_case__									=     num_queries
											snake_case__									=     no_object_weight
											snake_case__									=     class_weight
											snake_case__									=     mask_weight
											snake_case__									=     dice_weight
											snake_case__									=     train_num_points
											snake_case__									=     oversample_ratio
											snake_case__									=     importance_sample_ratio
											snake_case__									=     init_std
											snake_case__									=     init_xavier_std
											snake_case__									=     use_auxiliary_loss
											snake_case__									=     feature_strides
											snake_case__									=     output_auxiliary_logits
											snake_case__									=     decoder_layers

											super().__init__(**UpperCamelCase						)
							@classmethod
							def lowerCAmelCase_ (     cls:      Any       ,	UpperCamelCase:      PretrainedConfig       ,	**UpperCamelCase:      Tuple						)		->		Tuple:
											return cls(
											    backbone_config=UpperCamelCase       ,	**UpperCamelCase       ,	)


							def lowerCAmelCase_ (     self:      Any						)		->		Dict[str, any]:
											snake_case__									=     copy.deepcopy(self.__dict__						)
											snake_case__									=     self.backbone_config.to_dict()
											snake_case__									=     self.__class__.model_type
											return output












from __future__ import annotations
def      a_ (      _A  )			->			bool:







				"""simple docstring"""
				return len(set(_A  )  ) == len(_A  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




