"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    '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   __snake_case			(	_UpperCAmelCase							):
      a__								=						"layoutlmv2"



      def __init__(		self       ,     lowercase=3_05_22       ,     lowercase=7_68       ,     lowercase=12       ,     lowercase=12       ,     lowercase=30_72       ,     lowercase="gelu"       ,     lowercase=0.1       ,     lowercase=0.1       ,     lowercase=5_12       ,     lowercase=2       ,     lowercase=0.02       ,     lowercase=1e-12       ,     lowercase=0       ,     lowercase=10_24       ,     lowercase=1_28       ,     lowercase=32       ,     lowercase=True       ,     lowercase=2_56       ,     lowercase=64       ,     lowercase=True       ,     lowercase=[7, 7, 2_56]       ,     lowercase=1_28       ,     lowercase=1_28       ,     lowercase=True       ,     lowercase=True       ,     lowercase=False       ,     lowercase=None       ,     **lowercase       ,     )      ->     Any:





        '''simple docstring'''





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



      @classmethod
      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        return {
            "MODEL.MASK_ON": True,
            "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
            "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
            "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
            "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
            "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
            "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
            "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
            "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
            "MODEL.POST_NMS_TOPK_TEST": 10_00,
            "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": 1_01,
            "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
            "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     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





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

        return detectrona_config










def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						if not root or root not in binary_tree_mirror_dictionary:
												return
						_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    binary_tree_mirror_dictionary[root][:2]
						_SCREAMING_SNAKE_CASE       =    [right_child, left_child]
						binary_tree_mirror_dict(snake_case__			,snake_case__						)
						binary_tree_mirror_dict(snake_case__			,snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__ = 1						)				->    dict:






						"""simple docstring"""





						if not binary_tree:
												raise ValueError("""binary tree cannot be empty"""						)
						if root not in binary_tree:
												_SCREAMING_SNAKE_CASE       =    F'root {root} is not present in the binary_tree'
												raise ValueError(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    dict(snake_case__						)
						binary_tree_mirror_dict(snake_case__			,snake_case__						)
						return binary_tree_mirror_dictionary


if __name__ == "__main__":
		UpperCamelCase												=      {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
		print(f"Binary tree: {binary_tree}")
		UpperCamelCase												=      binary_tree_mirror(binary_tree, 5)
		print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")

