



'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_A					:     List[Any]      =logging.get_logger(__name__)

_A					:     Optional[Any]      ={
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
		import detectrona



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    """layoutlmv2"""




					def __init__(	self					:						List[str]  ,	UpperCamelCase_					:						int=3_0522  ,	UpperCamelCase_					:						Optional[Any]=768  ,	UpperCamelCase_					:						Tuple=12  ,	UpperCamelCase_					:						Union[str, Any]=12  ,	UpperCamelCase_					:						List[str]=3072  ,	UpperCamelCase_					:						List[str]="gelu"  ,	UpperCamelCase_					:						List[Any]=0.1  ,	UpperCamelCase_					:						List[Any]=0.1  ,	UpperCamelCase_					:						Optional[Any]=512  ,	UpperCamelCase_					:						int=2  ,	UpperCamelCase_					:						Optional[int]=0.02  ,	UpperCamelCase_					:						int=1E-12  ,	UpperCamelCase_					:						List[Any]=0  ,	UpperCamelCase_					:						Union[str, Any]=1024  ,	UpperCamelCase_					:						int=128  ,	UpperCamelCase_					:						Dict=32  ,	UpperCamelCase_					:						int=True  ,	UpperCamelCase_					:						Union[str, Any]=256  ,	UpperCamelCase_					:						int=64  ,	UpperCamelCase_					:						Union[str, Any]=True  ,	UpperCamelCase_					:						int=[7, 7, 256]  ,	UpperCamelCase_					:						Optional[int]=128  ,	UpperCamelCase_					:						int=128  ,	UpperCamelCase_					:						List[str]=True  ,	UpperCamelCase_					:						Any=True  ,	UpperCamelCase_					:						Optional[int]=False  ,	UpperCamelCase_					:						Union[str, Any]=None  ,	**UpperCamelCase_					:						Optional[int]  ,	)	->							Optional[int]:



						'''simple docstring'''





						super().__init__(
						    vocab_size=a_  ,	hidden_size=a_  ,	num_hidden_layers=a_  ,	num_attention_heads=a_  ,	intermediate_size=a_  ,	hidden_act=a_  ,	hidden_dropout_prob=a_  ,	attention_probs_dropout_prob=a_  ,	max_position_embeddings=a_  ,	type_vocab_size=a_  ,	initializer_range=a_  ,	layer_norm_eps=a_  ,	pad_token_id=a_  ,	**a_  ,	)
						_lowercase       :							Optional[int]  	=		max_ad_position_embeddings
						_lowercase       :							Union[str, Any]  	=		max_rel_pos
						_lowercase       :							Optional[Any]  	=		rel_pos_bins
						_lowercase       :							Union[str, Any]  	=		fast_qkv
						_lowercase       :							int  	=		max_rel_ad_pos
						_lowercase       :							Union[str, Any]  	=		rel_ad_pos_bins
						_lowercase       :							Optional[int]  	=		convert_sync_batchnorm
						_lowercase       :							Union[str, Any]  	=		image_feature_pool_shape
						_lowercase       :							List[Any]  	=		coordinate_size
						_lowercase       :							Optional[Any]  	=		shape_size
						_lowercase       :							Optional[Any]  	=		has_relative_attention_bias
						_lowercase       :							Dict  	=		has_spatial_attention_bias
						_lowercase       :							Dict  	=		has_visual_segment_embedding
						_lowercase       :							Optional[int]  	=		(
						    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
						)




					@classmethod
					def     __UpperCAmelCase  (	self					:						Union[str, Any]       )	->							Union[str, Any]:



						'''simple docstring'''





						return {
						    "MODEL.MASK_ON": True,
						    "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
						    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
						    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
						    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
						    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
						    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
						    "MODEL.POST_NMS_TOPK_TEST": 1000,
						    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
						    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
						    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
						    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
						    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
						    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
						    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
						    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
						    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
						    "MODEL.RESNETS.DEPTH": 101,
						    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
						    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.RESNETS.NUM_GROUPS": 32,
						    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
						    "MODEL.RESNETS.STRIDE_IN_1X1": False,
						}




					def     __UpperCAmelCase  (	self					:						Optional[Any]       )	->							Any:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		detectrona.config.get_cfg()
						for k, v in self.detectrona_config_args.items():
							_lowercase       :							List[Any]  	=		k.split('.'       )
							_lowercase       :							List[str]  	=		detectrona_config
							for attribute in attributes[:-1]:
								_lowercase       :							Optional[Any]  	=		getattr(a_  ,	a_       )
							setattr(a_  ,	attributes[-1]  ,	a_       )

						return detectrona_config





from __future__ import annotations

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_ = 0		):
											a__					   =		value
											a__					   =		None
											a__					   =		None




def       A_   (						__a  :					Node | None			,		__a  :					Node | None		):

				"""simple docstring"""
				if treea is None:
								return treea
				if treea is None:
								return treea

				a__					   =		treea.value + treea.value
				a__					   =		merge_two_binary_trees(treea.left			,		treea.left		)
				a__					   =		merge_two_binary_trees(treea.right			,		treea.right		)
				return treea

def       A_   (						__a  :					Node | None		):

				"""simple docstring"""
				if root:
								print(root.value		)
								print_preorder(root.left		)
								print_preorder(root.right		)


if __name__ == "__main__":
	UpperCAmelCase							=       Node(1)
	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(3)
	UpperCAmelCase							=       Node(4)

	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(4)
	UpperCAmelCase							=       Node(6)
	UpperCAmelCase							=       Node(9)
	UpperCAmelCase							=       Node(5)

	print("""Tree1 is: """)
	print_preorder(treea)
	print("""Tree2 is: """)
	print_preorder(treea)
	UpperCAmelCase							=       merge_two_binary_trees(treea, treea)
	print("""Merged Tree is: """)
	print_preorder(merged_tree)

