"""simple docstring"""





from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any


@dataclass
class      UpperCAmelCase_	:
      lowerCamelCase__		:    int
      lowerCamelCase__		:    Node | None   =  None
      lowerCamelCase__		:    Node | None   =  None





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	Node(1     )
       lowercase__			:		List[str]       =	Node(2     )
       lowercase__			:		List[str]       =	Node(3     )
       lowercase__			:		int       =	Node(4     )
       lowercase__			:		Tuple       =	Node(5     )
       return tree





def 				a_     (      _lowerCAmelCase  : Node | None     ):





       '''simple docstring'''

       return [root.data, *preorder(root.left     ), *preorder(root.right     )] if root else []





def 				a_     (      _lowerCAmelCase  : Node | None     ):





       '''simple docstring'''

       return postorder(root.left     ) + postorder(root.right     ) + [root.data] if root else []





def 				a_     (      _lowerCAmelCase  : Node | None     ):





       '''simple docstring'''

       return [*inorder(root.left     ), root.data, *inorder(root.right     )] if root else []





def 				a_     (      _lowerCAmelCase  : Node | None     ):





       '''simple docstring'''

       return (max(height(root.left     )   , height(root.right     )     ) + 1) if root else 0





def 				a_     (      _lowerCAmelCase  : Node | None     ):





       '''simple docstring'''

       lowercase__			:		list[Any]       =	[]

       if root is None:
              return output

       lowercase__			:		Dict       =	deque([root]     )

       while process_queue:
              lowercase__			:		str       =	process_queue.popleft()
              output.append(node.data     )

              if node.left:
                     process_queue.append(node.left     )
              if node.right:
                     process_queue.append(node.right     )
       return output





def 				a_     (      _lowerCAmelCase  : Node | None   , _lowerCAmelCase  : int     ):





       '''simple docstring'''

       lowercase__			:		list[Any]       =	[]

       def populate_output(_lowerCAmelCase  : Node | None   , _lowerCAmelCase  : int     ) -> None:
              if not root:
                     return
              if level == 1:
                     output.append(root.data     )
              elif level > 1:
                     populate_output(root.left   , level - 1     )
                     populate_output(root.right   , level - 1     )

       populate_output(_lowerCAmelCase   , _lowerCAmelCase     )
       return output





def 				a_     (      _lowerCAmelCase  : Node | None   , _lowerCAmelCase  : int     ):





       '''simple docstring'''

       lowercase__			:		list[Any]       =	[]

       def populate_output(_lowerCAmelCase  : Node | None   , _lowerCAmelCase  : int     ) -> None:
              if root is None:
                     return
              if level == 1:
                     output.append(root.data     )
              elif level > 1:
                     populate_output(root.right   , level - 1     )
                     populate_output(root.left   , level - 1     )

       populate_output(_lowerCAmelCase   , _lowerCAmelCase     )
       return output





def 				a_     (      _lowerCAmelCase  : Node | None     ):





       '''simple docstring'''

       if root is None:
              return []

       lowercase__			:		list[Sequence[Node | None]]       =	[]

       lowercase__			:		Any       =	0
       lowercase__			:		Dict       =	height(_lowerCAmelCase     )

       for h in range(1   , height_tree + 1     ):
              if not flag:
                     output.append(get_nodes_from_left_to_right(_lowerCAmelCase   , _lowerCAmelCase     )     )
                     lowercase__			:		Any       =	1
              else:
                     output.append(get_nodes_from_right_to_left(_lowerCAmelCase   , _lowerCAmelCase     )     )
                     lowercase__			:		List[Any]       =	0

       return output





def 				a_     (      ):  # Main function for testing.





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	make_tree()
       print(f"""In-order Traversal: {inorder(_lowerCAmelCase     )}"""     )
       print(f"""Pre-order Traversal: {preorder(_lowerCAmelCase     )}"""     )
       print(f"""Post-order Traversal: {postorder(_lowerCAmelCase     )}"""   , '\n'     )

       print(f"""Height of Tree: {height(_lowerCAmelCase     )}"""   , '\n'     )

       print('Complete Level Order Traversal: '     )
       print(level_order(_lowerCAmelCase     )   , '\n'     )

       print('Level-wise order Traversal: '     )

       for level in range(1   , height(_lowerCAmelCase     ) + 1     ):
              print(f"""Level {level}:"""   , get_nodes_from_left_to_right(_lowerCAmelCase   , level=_lowerCAmelCase     )     )

       print('\nZigZag order Traversal: '     )
       print(zigzag(_lowerCAmelCase     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()








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


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






					super().__init__(
					    vocab_size=__lowerCAmelCase							,	hidden_size=__lowerCAmelCase							,	num_hidden_layers=__lowerCAmelCase							,	num_attention_heads=__lowerCAmelCase							,	intermediate_size=__lowerCAmelCase							,	hidden_act=__lowerCAmelCase							,	hidden_dropout_prob=__lowerCAmelCase							,	attention_probs_dropout_prob=__lowerCAmelCase							,	max_position_embeddings=__lowerCAmelCase							,	type_vocab_size=__lowerCAmelCase							,	initializer_range=__lowerCAmelCase							,	layer_norm_eps=__lowerCAmelCase							,	pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					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    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






			@property
			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	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
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__lowerCAmelCase   )
					# Generate dummy inputs according to compute batch and sequence
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * 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)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



