






from __future__ import annotations

import math
import random
from typing import Any



class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self       )     ->							None:
							UpperCAmelCase_   :				list[Any]						=						[]
							UpperCAmelCase_   :				int						=						0
							UpperCAmelCase_   :				int						=						0



		def  __UpperCAmelCase      (							self       )     ->							bool:
							return self.head == self.tail



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							self.data.append(_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[int]						=						self.tail + 1



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				List[Any]						=						self.data[self.head]
							UpperCAmelCase_   :				Dict						=						self.head + 1
							return ret



		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.tail - self.head



		def  __UpperCAmelCase      (							self       )     ->							None:
							print(self.data       )
							print('**************'       )
							print(self.data[self.head : self.tail]       )





class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				List[Any]						=						data
							UpperCAmelCase_   :				MyNode | None						=						None
							UpperCAmelCase_   :				MyNode | None						=						None
							UpperCAmelCase_   :				int						=						1



		def  __UpperCAmelCase      (							self       )     ->							Any:
							return self.data



		def  __UpperCAmelCase      (							self       )     ->							MyNode | None:
							return self.left



		def  __UpperCAmelCase      (							self       )     ->							MyNode | None:
							return self.right



		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.height



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				Dict						=						data



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				Tuple						=						node



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				Optional[int]						=						node



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				int						=						height





def 					lowercase__				(					__snake_case   :							MyNode | None  ):


					'''simple docstring'''


					if node is None:
										return 0
					return node.get_height()




def 					lowercase__				(					__snake_case   :							int					,	__snake_case   :							int  ):


					'''simple docstring'''


					if a > b:
										return a
					return b




def 					lowercase__				(					__snake_case   :							MyNode  ):


					'''simple docstring'''


					print('left rotation node:'					,	node.get_data()  )
					UpperCAmelCase_   :				Union[str, Any]						=						node.get_left()
					assert ret is not None
					node.set_left(ret.get_right()  )
					ret.set_right(__snake_case  )
					UpperCAmelCase_   :				Optional[Any]						=						my_max(get_height(node.get_right()  )					,	get_height(node.get_left()  )  ) + 1
					node.set_height(__snake_case  )
					UpperCAmelCase_   :				Tuple						=						my_max(get_height(ret.get_right()  )					,	get_height(ret.get_left()  )  ) + 1
					ret.set_height(__snake_case  )
					return ret




def 					lowercase__				(					__snake_case   :							MyNode  ):


					'''simple docstring'''


					print('right rotation node:'					,	node.get_data()  )
					UpperCAmelCase_   :				List[Any]						=						node.get_right()
					assert ret is not None
					node.set_right(ret.get_left()  )
					ret.set_left(__snake_case  )
					UpperCAmelCase_   :				int						=						my_max(get_height(node.get_right()  )					,	get_height(node.get_left()  )  ) + 1
					node.set_height(__snake_case  )
					UpperCAmelCase_   :				Any						=						my_max(get_height(ret.get_right()  )					,	get_height(ret.get_left()  )  ) + 1
					ret.set_height(__snake_case  )
					return ret




def 					lowercase__				(					__snake_case   :							MyNode  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						node.get_left()
					assert left_child is not None
					node.set_left(left_rotation(__snake_case  )  )
					return right_rotation(__snake_case  )




def 					lowercase__				(					__snake_case   :							MyNode  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[str]						=						node.get_right()
					assert right_child is not None
					node.set_right(right_rotation(__snake_case  )  )
					return left_rotation(__snake_case  )




def 					lowercase__				(					__snake_case   :							MyNode | None					,	__snake_case   :							Any  ):


					'''simple docstring'''


					if node is None:
										return MyNode(__snake_case  )
					if data < node.get_data():
										node.set_left(insert_node(node.get_left()					,	__snake_case  )  )
										if (
										    get_height(node.get_left()  ) - get_height(node.get_right()  ) == 2
										):  # an unbalance detected
															UpperCAmelCase_   :				Any						=						node.get_left()
															assert left_child is not None
															if (
															    data < left_child.get_data()
															):  # new node is the left child of the left child
																				UpperCAmelCase_   :				Any						=						right_rotation(__snake_case  )
															else:
																				UpperCAmelCase_   :				Optional[int]						=						lr_rotation(__snake_case  )
					else:
										node.set_right(insert_node(node.get_right()					,	__snake_case  )  )
										if get_height(node.get_right()  ) - get_height(node.get_left()  ) == 2:
															UpperCAmelCase_   :				Optional[Any]						=						node.get_right()
															assert right_child is not None
															if data < right_child.get_data():
																				UpperCAmelCase_   :				Dict						=						rl_rotation(__snake_case  )
															else:
																				UpperCAmelCase_   :				Optional[int]						=						left_rotation(__snake_case  )
					UpperCAmelCase_   :				List[str]						=						my_max(get_height(node.get_right()  )					,	get_height(node.get_left()  )  ) + 1
					node.set_height(__snake_case  )
					return node




def 					lowercase__				(					__snake_case   :							MyNode  ):


					'''simple docstring'''


					while True:
										UpperCAmelCase_   :				Optional[Any]						=						root.get_right()
										if right_child is None:
															break
										UpperCAmelCase_   :				List[str]						=						right_child
					return root.get_data()




def 					lowercase__				(					__snake_case   :							MyNode  ):


					'''simple docstring'''


					while True:
										UpperCAmelCase_   :				Optional[Any]						=						root.get_left()
										if left_child is None:
															break
										UpperCAmelCase_   :				Dict						=						left_child
					return root.get_data()




def 					lowercase__				(					__snake_case   :							MyNode					,	__snake_case   :							Any  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						root.get_left()
					UpperCAmelCase_   :				Dict						=						root.get_right()
					if root.get_data() == data:
										if left_child is not None and right_child is not None:
															UpperCAmelCase_   :				Any						=						get_left_most(__snake_case  )
															root.set_data(__snake_case  )
															root.set_right(del_node(__snake_case					,	__snake_case  )  )
										elif left_child is not None:
															UpperCAmelCase_   :				Optional[int]						=						left_child
										elif right_child is not None:
															UpperCAmelCase_   :				Tuple						=						right_child
										else:
															return None
					elif root.get_data() > data:
										if left_child is None:
															print('No such data'  )
															return root
										else:
															root.set_left(del_node(__snake_case					,	__snake_case  )  )
					else:  # root.get_data() < data
										if right_child is None:
															return root
										else:
															root.set_right(del_node(__snake_case					,	__snake_case  )  )

					if get_height(__snake_case  ) - get_height(__snake_case  ) == 2:
										assert right_child is not None
										if get_height(right_child.get_right()  ) > get_height(right_child.get_left()  ):
															UpperCAmelCase_   :				Union[str, Any]						=						left_rotation(__snake_case  )
										else:
															UpperCAmelCase_   :				Union[str, Any]						=						rl_rotation(__snake_case  )
					elif get_height(__snake_case  ) - get_height(__snake_case  ) == -2:
										assert left_child is not None
										if get_height(left_child.get_left()  ) > get_height(left_child.get_right()  ):
															UpperCAmelCase_   :				List[str]						=						right_rotation(__snake_case  )
										else:
															UpperCAmelCase_   :				Union[str, Any]						=						lr_rotation(__snake_case  )
					UpperCAmelCase_   :				Dict						=						my_max(get_height(root.get_right()  )					,	get_height(root.get_left()  )  ) + 1
					root.set_height(__snake_case  )
					return root



class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self       )     ->							None:
							UpperCAmelCase_   :				MyNode | None						=						None



		def  __UpperCAmelCase      (							self       )     ->							int:
							return get_height(self.root       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							print('insert:' + str(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Tuple						=						insert_node(self.root    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							print('delete:' + str(_UpperCamelCase       )       )
							if self.root is None:
												print('Tree is empty!'       )
												return
							UpperCAmelCase_   :				Tuple						=						del_node(self.root    ,		_UpperCamelCase       )



		def __str__(							self    ,		)     ->							str:  # a level traversale, gives a more intuitive look on the tree
							UpperCAmelCase_   :				int						=						''
							UpperCAmelCase_   :				Optional[int]						=						MyQueue()
							q.push(self.root       )
							UpperCAmelCase_   :				Optional[Any]						=						self.get_height()
							if layer == 0:
												return output
							UpperCAmelCase_   :				int						=						0
							while not q.is_empty():
												UpperCAmelCase_   :				str						=						q.pop()
												UpperCAmelCase_   :				str						=						' ' * int(math.pow(2    ,		layer - 1       )       )
												output += space
												if node is None:
																	output += "*"
																	q.push(_UpperCamelCase       )
																	q.push(_UpperCamelCase       )
												else:
																	output += str(node.get_data()       )
																	q.push(node.get_left()       )
																	q.push(node.get_right()       )
												output += space
												UpperCAmelCase_   :				List[str]						=						cnt + 1
												for i in range(1_0_0       ):
																	if cnt == math.pow(2    ,		_UpperCamelCase       ) - 1:
																						UpperCAmelCase_   :				Dict						=						layer - 1
																						if layer == 0:
																											output += "\n*************************************"
																											return output
																						output += "\n"
																						break
							output += "\n*************************************"
							return output





def 					lowercase__				(					):


					'''simple docstring'''


					import doctest

					doctest.testmod()


if __name__ == "__main__":
		_test()
		__UpperCAmelCase     							=						AVLtree()
		__UpperCAmelCase     							=						list(range(10))
		random.shuffle(lst)
		for i in lst:
				t.insert(i)
				print(str(t))
		random.shuffle(lst)
		for i in lst:
				t.del_node(i)
				print(str(t))














from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
		from ...processing_utils import ProcessorMixin
		from ...utils import TensorType


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  '''layoutlmv3'''



		def __init__(							self    ,		_UpperCamelCase=5_0_2_6_5    ,		_UpperCamelCase=7_6_8    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=3_0_7_2    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1E-5    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=True    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=2_5_6    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=None    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:
							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    ,		bos_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				str						=						max_ad_position_embeddings
							UpperCAmelCase_   :				Union[str, Any]						=						coordinate_size
							UpperCAmelCase_   :				Union[str, Any]						=						shape_size
							UpperCAmelCase_   :				str						=						has_relative_attention_bias
							UpperCAmelCase_   :				Tuple						=						rel_pos_bins
							UpperCAmelCase_   :				Dict						=						max_rel_pos
							UpperCAmelCase_   :				Any						=						has_spatial_attention_bias
							UpperCAmelCase_   :				Optional[Any]						=						rel_ad_pos_bins
							UpperCAmelCase_   :				List[str]						=						max_rel_ad_pos
							UpperCAmelCase_   :				List[str]						=						text_embed
							UpperCAmelCase_   :				Dict						=						visual_embed
							UpperCAmelCase_   :				Optional[int]						=						input_size
							UpperCAmelCase_   :				List[Any]						=						num_channels
							UpperCAmelCase_   :				List[Any]						=						patch_size
							UpperCAmelCase_   :				Union[str, Any]						=						classifier_dropout





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  version.parse('''1.12'''      )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							# The order of inputs is different for question answering and sequence classification
							if self.task in ["question-answering", "sequence-classification"]:
												return OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'sequence'}),
												        ('bbox', {0: 'batch', 1: 'sequence'}),
												        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
												    ]       )
							else:
												return OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'sequence'}),
												        ('bbox', {0: 'batch', 1: 'sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'sequence'}),
												        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
												    ]       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 1E-5



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_2



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 3    ,		_UpperCamelCase = 4_0    ,		_UpperCamelCase = 4_0    ,		)     ->							Mapping[str, Any]:
							setattr(processor.image_processor    ,		'apply_ocr'    ,		_UpperCamelCase       )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Optional[int]						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_batch    ,		num_token_to_add=0       )
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Any						=						processor.tokenizer.num_special_tokens_to_add(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=_UpperCamelCase       )
							# Generate dummy inputs according to compute batch and sequence
							UpperCAmelCase_   :				Tuple						=						[[' '.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							UpperCAmelCase_   :				List[str]						=						[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
							UpperCAmelCase_   :				str						=						self._generate_dummy_images(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						dict(
							    processor(
							        _UpperCamelCase    ,		text=_UpperCamelCase    ,		boxes=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		)       )

							return inputs






