


"""simple docstring"""







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__    :							Any            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Any    =      "mask2former"
	snake_case__    :  Any    =      ["swin"]
	snake_case__    :  Optional[Any]    =      {"hidden_size": "hidden_dim"}




	def __init__(			self      :    Tuple      ,      UpperCAmelCase__      :    Optional[Dict] = None      ,      UpperCAmelCase__      :    int = 2_5_6      ,      UpperCAmelCase__      :    int = 2_5_6      ,      UpperCAmelCase__      :    int = 2_5_6      ,      UpperCAmelCase__      :    int = 1_0_2_4      ,      UpperCAmelCase__      :    str = "relu"      ,      UpperCAmelCase__      :    int = 6      ,      UpperCAmelCase__      :    int = 1_0      ,      UpperCAmelCase__      :    int = 8      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 2_0_4_8      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    int = 2_5_5      ,      UpperCAmelCase__      :    int = 1_0_0      ,      UpperCAmelCase__      :    float = 0.1      ,      UpperCAmelCase__      :    float = 2.0      ,      UpperCAmelCase__      :    float = 5.0      ,      UpperCAmelCase__      :    float = 5.0      ,      UpperCAmelCase__      :    int = 1_2_5_4_4      ,      UpperCAmelCase__      :    float = 3.0      ,      UpperCAmelCase__      :    float = 0.75      ,      UpperCAmelCase__      :    float = 0.02      ,      UpperCAmelCase__      :    float = 1.0      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    List[int] = [4, 8, 1_6, 3_2]      ,      UpperCAmelCase__      :    bool = None      ,      **UpperCAmelCase__      :    List[Any]      ,      )	->     Optional[int]:
						if backbone_config is None:
											logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."       )
											__SCREAMING_SNAKE_CASE						       =    CONFIG_MAPPING["swin"](
											    image_size=2_2_4      ,      in_channels=3      ,      patch_size=4      ,      embed_dim=9_6      ,      depths=[2, 2, 1_8, 2]      ,      num_heads=[3, 6, 1_2, 2_4]      ,      window_size=7      ,      drop_path_rate=0.3      ,      use_absolute_embeddings=UpperCAmelCase__      ,      out_features=["stage1", "stage2", "stage3", "stage4"]      ,      )

						if isinstance(UpperCAmelCase__      ,      UpperCAmelCase__       ):
											__SCREAMING_SNAKE_CASE						       =    backbone_config.pop("model_type"       )
											__SCREAMING_SNAKE_CASE						       =    CONFIG_MAPPING[backbone_model_type]
											__SCREAMING_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       )}"""       )

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

						super().__init__(**UpperCAmelCase__       )




	@classmethod
	def   UpperCAmelCase_				(			cls      :    Union[str, Any]      ,      UpperCAmelCase__      :    PretrainedConfig      ,      **UpperCAmelCase__      :    int       )	->     Dict:
						return cls(
						    backbone_config=UpperCAmelCase__      ,      **UpperCAmelCase__      ,      )




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




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return " ".join(
					    "".join(word[::-1]       ) if len(lowerCAmelCase_       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words('''Hey wollef sroirraw'''))
