"""simple docstring"""




import copy
from typing import Dict, Optional

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


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    """shi-labs/oneformer_ade20k_swin_tiny""": (
        """https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"""
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    Optional[Any]    				=		'''oneformer'''
						lowerCAmelCase__	:    Optional[int]    				=		{'''hidden_size''': '''hidden_dim'''}





						def __init__(							self		,__UpperCAmelCase = None		,__UpperCAmelCase = 2_55		,__UpperCAmelCase = 1_50		,__UpperCAmelCase = 0.1		,__UpperCAmelCase = 2.0		,__UpperCAmelCase = 5.0		,__UpperCAmelCase = 5.0		,__UpperCAmelCase = 0.5		,__UpperCAmelCase = 0.0_7		,__UpperCAmelCase = 1_25_44		,__UpperCAmelCase = 3.0		,__UpperCAmelCase = 0.7_5		,__UpperCAmelCase = 0.0_2		,__UpperCAmelCase = 1.0		,__UpperCAmelCase = 1e-05		,__UpperCAmelCase = False		,__UpperCAmelCase = True		,__UpperCAmelCase = True		,__UpperCAmelCase = [4, 8, 16, 32]		,__UpperCAmelCase = 77		,__UpperCAmelCase = 2_56		,__UpperCAmelCase = 77		,__UpperCAmelCase = 6		,__UpperCAmelCase = 4_94_08		,__UpperCAmelCase = 2		,__UpperCAmelCase = 16		,__UpperCAmelCase = 2_56		,__UpperCAmelCase = 2_56		,__UpperCAmelCase = 2_56		,__UpperCAmelCase = 10_24		,__UpperCAmelCase = "GN"		,__UpperCAmelCase = 6		,__UpperCAmelCase = 10		,__UpperCAmelCase = True		,__UpperCAmelCase = 8		,__UpperCAmelCase = 0.1		,__UpperCAmelCase = 20_48		,__UpperCAmelCase = False		,__UpperCAmelCase = False		,__UpperCAmelCase = 2		,__UpperCAmelCase = 4		,**__UpperCAmelCase		,)       ->					str:







													if backbone_config is None:
																				logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'  )
																				A__						  =				CONFIG_MAPPING['''swin'''](
																				    image_size=2_24		,in_channels=3		,patch_size=4		,embed_dim=96		,depths=[2, 2, 6, 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']		,)
													elif isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				backbone_config.get('model_type'  )
																				A__						  =				CONFIG_MAPPING[backbone_model_type]
																				A__						  =				config_class.from_dict(__UpperCAmelCase  )

													A__						  =				backbone_config

													A__						  =				ignore_value
													A__						  =				num_queries
													A__						  =				no_object_weight
													A__						  =				class_weight
													A__						  =				mask_weight
													A__						  =				dice_weight
													A__						  =				contrastive_weight
													A__						  =				contrastive_temperature
													A__						  =				train_num_points
													A__						  =				oversample_ratio
													A__						  =				importance_sample_ratio
													A__						  =				init_std
													A__						  =				init_xavier_std
													A__						  =				layer_norm_eps
													A__						  =				is_training
													A__						  =				use_auxiliary_loss
													A__						  =				output_auxiliary_logits
													A__						  =				strides
													A__						  =				task_seq_len
													A__						  =				text_encoder_width
													A__						  =				text_encoder_context_length
													A__						  =				text_encoder_num_layers
													A__						  =				text_encoder_vocab_size
													A__						  =				text_encoder_proj_layers
													A__						  =				text_encoder_n_ctx
													A__						  =				conv_dim
													A__						  =				mask_dim
													A__						  =				hidden_dim
													A__						  =				encoder_feedforward_dim
													A__						  =				norm
													A__						  =				encoder_layers
													A__						  =				decoder_layers
													A__						  =				use_task_norm
													A__						  =				num_attention_heads
													A__						  =				dropout
													A__						  =				dim_feedforward
													A__						  =				pre_norm
													A__						  =				enforce_input_proj
													A__						  =				query_dec_layers
													A__						  =				common_stride
													A__						  =				decoder_layers

													super().__init__(**__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					str:
													A__						  =				copy.deepcopy(self.__dict__  )
													A__						  =				self.backbone_config.to_dict()
													A__						  =				self.__class__.model_type
													return output













"""simple docstring"""





from __future__ import annotations


class   _lowercase			:


      def __init__(     self					:						int				, a					:						int = 0    ):





            """simple docstring"""







            __snake_case   :     Optional[Any]	      =value
            __snake_case   :     Node | None	      =None
            __snake_case   :     Node | None	      =None



def      __lowercase       (     a			:					Node | None							,		a			:					Node | None			)       ->       Node | None:
      if treea is None:
            return treea
      if treea is None:
            return treea

      __snake_case   :     Dict	      =treea.value + treea.value
      __snake_case   :     int	      =merge_two_binary_trees(treea.left							,		treea.left			)
      __snake_case   :     List[str]	      =merge_two_binary_trees(treea.right							,		treea.right			)
      return treea



def      __lowercase       (     a			:					Node | None			)       ->       None:
      if root:
            print(root.value			)
            print_preorder(root.left			)
            print_preorder(root.right			)


if __name__ == "__main__":
    UpperCamelCase_						:					List[str]      				=			Node(1)
    UpperCamelCase_						:					List[Any]      				=			Node(2)
    UpperCamelCase_						:					Optional[Any]      				=			Node(3)
    UpperCamelCase_						:					int      				=			Node(4)

    UpperCamelCase_						:					int      				=			Node(2)
    UpperCamelCase_						:					List[str]      				=			Node(4)
    UpperCamelCase_						:					Any      				=			Node(6)
    UpperCamelCase_						:					Union[str, Any]      				=			Node(9)
    UpperCamelCase_						:					Dict      				=			Node(5)

    print("""Tree1 is: """)
    print_preorder(treea)
    print("""Tree2 is: """)
    print_preorder(treea)
    UpperCamelCase_						:					Optional[Any]      				=			merge_two_binary_trees(treea, treea)
    print("""Merged Tree is: """)
    print_preorder(merged_tree)


