
from __future__ import annotations

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


@dataclass
class   __lowerCAmelCase		:



      """simple docstring"""







      A__		:	List[str]         =			4_2
      A__		:	Optional[Any]         =			None
      A__		:	Tuple         =			None






def 			UpperCAmelCase_     (  )       ->  Tuple:
    __lowercase						:	Any					=				Node(1		)
    __lowercase						:	List[str]					=				Node(2		)
    __lowercase						:	Optional[Any]					=				Node(3		)
    __lowercase						:	int					=				Node(4		)
    __lowercase						:	Any					=				Node(5		)
    return tree


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  List[Any]:
    return [root.data, *preorder(root.left		), *preorder(root.right		)] if root else []


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Tuple:
    return postorder(root.left		) + postorder(root.right		) + [root.data] if root else []


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  int:
    return [*inorder(root.left		), root.data, *inorder(root.right		)] if root else []


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  List[Any]:
    return (max(height(root.left		)		,    height(root.right		)		) + 1) if root else 0


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Any:
    __lowercase						:	list[Any]					=				[]

    if root is None:
        return output

    __lowercase						:	Optional[Any]					=				deque([root]		)

    while process_queue:
        __lowercase						:	Optional[int]					=				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 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  List[Any]:
    __lowercase						:	list[Any]					=				[]

    def populate_output(__lowerCAmelCase		,    __lowerCAmelCase		) -> 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(__UpperCamelCase		,    __UpperCamelCase		)
    return output


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  str:
    __lowercase						:	list[Any]					=				[]

    def populate_output(__lowerCAmelCase		,    __lowerCAmelCase		) -> 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(__UpperCamelCase		,    __UpperCamelCase		)
    return output


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Any:
    if root is None:
        return []

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

    __lowercase						:	Any					=				0
    __lowercase						:	List[str]					=				height(__UpperCamelCase		)

    for h in range(1		,    height_tree + 1		):
        if not flag:
            output.append(get_nodes_from_left_to_right(__UpperCamelCase		,    __UpperCamelCase		)		)
            __lowercase						:	str					=				1
        else:
            output.append(get_nodes_from_right_to_left(__UpperCamelCase		,    __UpperCamelCase		)		)
            __lowercase						:	Optional[Any]					=				0

    return output


def 			UpperCAmelCase_     (  )       ->  Tuple:  # Main function for testing.
    __lowercase						:	Any					=				make_tree()
    print(F'In-order Traversal: {inorder(__UpperCamelCase		)}'		)
    print(F'Pre-order Traversal: {preorder(__UpperCamelCase		)}'		)
    print(F'Post-order Traversal: {postorder(__UpperCamelCase		)}'		,    '''\n'''		)

    print(F'Height of Tree: {height(__UpperCamelCase		)}'		,    '''\n'''		)

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

    print('''Level-wise order Traversal: '''		)

    for level in range(1		,    height(__UpperCamelCase		) + 1		):
        print(F'Level {level}:'		,    get_nodes_from_left_to_right(__UpperCamelCase		,    level=__UpperCamelCase		)		)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__  :  List[str]								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = '''maskformer-swin'''

							_a			      = {
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(       self     :       Union[str, Any]				,				A_     :       Dict=2_2_4				,				A_     :       Optional[Any]=4				,				A_     :       List[str]=3				,				A_     :       str=9_6				,				A_     :       Optional[Any]=[2, 2, 6, 2]				,				A_     :       Tuple=[3, 6, 1_2, 2_4]				,				A_     :       List[Any]=7				,				A_     :       List[Any]=4.0				,				A_     :       List[str]=True				,				A_     :       Dict=0.0				,				A_     :       int=0.0				,				A_     :       str=0.1				,				A_     :       Optional[int]="gelu"				,				A_     :       List[Any]=False				,				A_     :       int=0.02				,				A_     :       int=1e-5				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				**A_     :       List[Any]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     Dict									= image_size
									lowerCAmelCase_			:     Optional[Any]									= patch_size
									lowerCAmelCase_			:     Tuple									= num_channels
									lowerCAmelCase_			:     Any									= embed_dim
									lowerCAmelCase_			:     List[str]									= depths
									lowerCAmelCase_			:     Union[str, Any]									= len(A_)
									lowerCAmelCase_			:     List[str]									= num_heads
									lowerCAmelCase_			:     Dict									= window_size
									lowerCAmelCase_			:     Optional[int]									= mlp_ratio
									lowerCAmelCase_			:     Dict									= qkv_bias
									lowerCAmelCase_			:     str									= hidden_dropout_prob
									lowerCAmelCase_			:     List[str]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Optional[int]									= drop_path_rate
									lowerCAmelCase_			:     Any									= hidden_act
									lowerCAmelCase_			:     str									= use_absolute_embeddings
									lowerCAmelCase_			:     List[str]									= layer_norm_eps
									lowerCAmelCase_			:     int									= initializer_range
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									lowerCAmelCase_			:     str									= int(embed_dim * 2 ** (len(A_) - 1))
									lowerCAmelCase_			:     Optional[Any]									= ['''stem'''] + [F"""stage{idx}""" for idx in range(1				,				len(A_) + 1)]
									lowerCAmelCase_					,  lowerCAmelCase_			:     int									= get_aligned_output_features_output_indices(
									    out_features=A_				,				out_indices=A_				,				stage_names=self.stage_names)





