


"""simple docstring"""


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


a_											=			logging.get_logger(__name__)

a_											=			{
    "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):
		__UpperCamelCase							=	'layoutlmv2'







		def __init__(    self					:				Union[str, Any]			,      a__					:				Optional[Any]=3_05_22			,      a__					:				List[str]=7_68			,      a__					:				Optional[int]=12			,      a__					:				Union[str, Any]=12			,      a__					:				Dict=30_72			,      a__					:				List[str]="gelu"			,      a__					:				int=0.1			,      a__					:				Tuple=0.1			,      a__					:				Optional[int]=5_12			,      a__					:				str=2			,      a__					:				Optional[int]=0.0_2			,      a__					:				int=1E-1_2			,      a__					:				Union[str, Any]=0			,      a__					:				List[str]=10_24			,      a__					:				Optional[int]=1_28			,      a__					:				Dict=32			,      a__					:				Any=True			,      a__					:				List[Any]=2_56			,      a__					:				List[Any]=64			,      a__					:				Optional[int]=True			,      a__					:				str=[7, 7, 2_56]			,      a__					:				Optional[Any]=1_28			,      a__					:				Tuple=1_28			,      a__					:				Tuple=True			,      a__					:				Optional[int]=True			,      a__					:				Tuple=False			,      a__					:				Any=None			,      **a__					:				str			,      )				->  str:





						'''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__			,      )
						_A         =	max_ad_position_embeddings
						_A         =	max_rel_pos
						_A         =	rel_pos_bins
						_A         =	fast_qkv
						_A         =	max_rel_ad_pos
						_A         =	rel_ad_pos_bins
						_A         =	convert_sync_batchnorm
						_A         =	image_feature_pool_shape
						_A         =	coordinate_size
						_A         =	shape_size
						_A         =	has_relative_attention_bias
						_A         =	has_spatial_attention_bias
						_A         =	has_visual_segment_embedding
						_A         =	(
						    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
						)







		@classmethod
		def        a_       (    self					:				List[Any]						)				->  List[str]:





						'''simple docstring'''







						return {
						    "MODEL.MASK_ON": True,
						    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
						    "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        a_       (    self					:				Optional[Any]						)				->  int:





						'''simple docstring'''







						_A         =	detectrona.config.get_cfg()
						for k, v in self.detectrona_config_args.items():
										_A         =	k.split("."						)
										_A         =	detectrona_config
										for attribute in attributes[:-1]:
														_A         =	getattr(a__			,      a__						)
										setattr(a__			,      attributes[-1]			,      a__						)

						return detectrona_config



"""simple docstring"""


from __future__ import annotations

a_											=			[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class snake_case					:







		def __init__(    self					:				Optional[int]			,      a__					:				int						)				->  None:





						'''simple docstring'''







						_A         =	-1
						_A         =	-1
						_A         =	[-1] * length
						_A         =	None
						_A         =	None







		def __repr__(    self					:				Any						)				->  str:





						'''simple docstring'''







						return F"""Node(min_value={self.minn} max_value={self.maxx})"""






def    a__       ( __lowercase      )		->		Node | None:
				_A         =	Node(len(__lowercase      )      )
				_A     ,						_A         =	min(__lowercase      ), max(__lowercase      )
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				_A         =	(root.minn + root.maxx) // 2

				_A         =	[]
				_A         =	[]

				for index, num in enumerate(__lowercase      ):
								if num <= pivot:
												left_arr.append(__lowercase      )
								else:
												right_arr.append(__lowercase      )
								_A         =	len(__lowercase      )
				_A         =	build_tree(__lowercase      )
				_A         =	build_tree(__lowercase      )
				return root






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				_A         =	(node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left   ,	__lowercase   ,	node.map_left[index] - 1      )
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right   ,	__lowercase   ,	index - node.map_left[index]      )






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if start > end:
								return 0
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	__lowercase      )
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	start - 1      )
				return rank_till_end - rank_before_start






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				_A         =	node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left   ,	__lowercase   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	)
				else:
								return quantile(
								    node.right   ,	index - num_elements_in_left_tree   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	)






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				_A         =	range_counting(
				    node.left   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	__lowercase   ,	__lowercase   ,	)
				_A         =	range_counting(
				    node.right   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	__lowercase   ,	__lowercase   ,	)
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()