



from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 				__A					:
			UpperCamelCase					:int
			UpperCamelCase					:Node | None     =			None
			UpperCamelCase					:Node | None     =			None







def      _A				()  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		Node(1 )
			lowerCamelCase__ :      Dict								=		Node(2 )
			lowerCamelCase__ :      Union[str, Any]								=		Node(3 )
			lowerCamelCase__ :      Dict								=		Node(4 )
			lowerCamelCase__ :      List[str]								=		Node(5 )
			return tree






def      _A				(UpperCamelCase		:      Node | None )  ->Tuple:



			'''simple docstring'''







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






def      _A				(UpperCamelCase		:      Node | None )  ->str:



			'''simple docstring'''







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






def      _A				(UpperCamelCase		:      Node | None )  ->Dict:



			'''simple docstring'''







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






def      _A				(UpperCamelCase		:      Node | None )  ->int:



			'''simple docstring'''







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






def      _A				(UpperCamelCase		:      Node | None )  ->Optional[int]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		[]

			if root is None:
						return output

			lowerCamelCase__ :      Union[str, Any]								=		deque([root] )

			while process_queue:
						lowerCamelCase__ :      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				(UpperCamelCase		:      Node | None      ,			UpperCamelCase		:      int )  ->Union[str, Any]:



			'''simple docstring'''







			lowerCamelCase__ :      List[Any]								=		[]

			def populate_output(UpperCamelCase		:      Node | None      ,			UpperCamelCase		:      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(UpperCamelCase      ,			UpperCamelCase )
			return output






def      _A				(UpperCamelCase		:      Node | None      ,			UpperCamelCase		:      int )  ->Optional[int]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		[]

			def populate_output(UpperCamelCase		:      Node | None      ,			UpperCamelCase		:      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(UpperCamelCase      ,			UpperCamelCase )
			return output






def      _A				(UpperCamelCase		:      Node | None )  ->Optional[int]:



			'''simple docstring'''







			if root is None:
						return []

			lowerCamelCase__ :      Dict								=		[]

			lowerCamelCase__ :      int								=		0
			lowerCamelCase__ :      Any								=		height(UpperCamelCase )

			for h in range(1      ,			height_tree + 1 ):
						if not flag:
									output.append(get_nodes_from_left_to_right(UpperCamelCase      ,			UpperCamelCase ) )
									lowerCamelCase__ :      Optional[int]								=		1
						else:
									output.append(get_nodes_from_right_to_left(UpperCamelCase      ,			UpperCamelCase ) )
									lowerCamelCase__ :      int								=		0

			return output






def      _A				()  ->int:  # Main function for testing.



			'''simple docstring'''







			lowerCamelCase__ :      Optional[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()








import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


_lowercase     							=						logging.get_logger(__name__)






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Optional[int] )  ->Tuple:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		nn.functional.normalize(UpperCamelCase )
			lowerCamelCase__ :      Tuple								=		nn.functional.normalize(UpperCamelCase )
			return torch.mm(UpperCamelCase      ,			normalized_text_embeds.t() )
class 				__A					( A_      ):
			UpperCamelCase					:Optional[Any]     =			CLIPConfig

			UpperCamelCase					:Optional[Any]     =			['''CLIPEncoderLayer''']





			def __init__(self							,    __magic_name__				):
						super().__init__(__magic_name__				)

						lowerCamelCase__ :      Union[str, Any]								=		CLIPVisionModel(config.vision_config				)
						lowerCamelCase__ :      Any								=		nn.Linear(config.vision_config.hidden_size							,    config.projection_dim							,    bias=__magic_name__				)

						lowerCamelCase__ :      Any								=		nn.Parameter(torch.ones(17							,    config.projection_dim				)							,    requires_grad=__magic_name__				)
						lowerCamelCase__ :      Union[str, Any]								=		nn.Parameter(torch.ones(3							,    config.projection_dim				)							,    requires_grad=__magic_name__				)

						lowerCamelCase__ :      Any								=		nn.Parameter(torch.ones(17				)							,    requires_grad=__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		nn.Parameter(torch.ones(3				)							,    requires_grad=__magic_name__				)





			@torch.no_grad()
			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Any								=		self.vision_model(__magic_name__				)[1]  # pooled_output
						lowerCamelCase__ :      Tuple								=		self.visual_projection(__magic_name__				)

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						lowerCamelCase__ :      Any								=		cosine_distance(__magic_name__							,    self.special_care_embeds				).cpu().float().numpy()
						lowerCamelCase__ :      str								=		cosine_distance(__magic_name__							,    self.concept_embeds				).cpu().float().numpy()

						lowerCamelCase__ :      int								=		[]
						lowerCamelCase__ :      str								=		image_embeds.shape[0]
						for i in range(__magic_name__				):
									lowerCamelCase__ :      str								=		{"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

									# increase this value to create a stronger `nfsw` filter
									# at the cost of increasing the possibility of filtering benign images
									lowerCamelCase__ :      Optional[Any]								=		0.0

									for concept_idx in range(len(special_cos_dist[0]				)				):
												lowerCamelCase__ :      List[str]								=		special_cos_dist[i][concept_idx]
												lowerCamelCase__ :      Tuple								=		self.special_care_embeds_weights[concept_idx].item()
												lowerCamelCase__ :      str								=		round(concept_cos - concept_threshold + adjustment							,    3				)
												if result_img["special_scores"][concept_idx] > 0:
															result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}				)
															lowerCamelCase__ :      List[str]								=		0.01

									for concept_idx in range(len(cos_dist[0]				)				):
												lowerCamelCase__ :      str								=		cos_dist[i][concept_idx]
												lowerCamelCase__ :      Dict								=		self.concept_embeds_weights[concept_idx].item()
												lowerCamelCase__ :      int								=		round(concept_cos - concept_threshold + adjustment							,    3				)
												if result_img["concept_scores"][concept_idx] > 0:
															result_img["bad_concepts"].append(__magic_name__				)

									result.append(__magic_name__				)

						lowerCamelCase__ :      Tuple								=		[len(res["""bad_concepts"""]				) > 0 for res in result]

						return images, has_nsfw_concepts





			@torch.no_grad()
			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Union[str, Any]								=		self.vision_model(__magic_name__				)[1]  # pooled_output
						lowerCamelCase__ :      str								=		self.visual_projection(__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		cosine_distance(__magic_name__							,    self.special_care_embeds				)
						lowerCamelCase__ :      Dict								=		cosine_distance(__magic_name__							,    self.concept_embeds				)

						# increase this value to create a stronger `nsfw` filter
						# at the cost of increasing the possibility of filtering benign images
						lowerCamelCase__ :      Any								=		0.0

						lowerCamelCase__ :      Dict								=		special_cos_dist - self.special_care_embeds_weights + adjustment
						# special_scores = special_scores.round(decimals=3)
						lowerCamelCase__ :      Union[str, Any]								=		torch.any(special_scores > 0							,    dim=1				)
						lowerCamelCase__ :      Optional[Any]								=		special_care * 0.01
						lowerCamelCase__ :      Dict								=		special_adjustment.unsqueeze(1				).expand(-1							,    cos_dist.shape[1]				)

						lowerCamelCase__ :      Tuple								=		(cos_dist - self.concept_embeds_weights) + special_adjustment
						# concept_scores = concept_scores.round(decimals=3)
						lowerCamelCase__ :      Any								=		torch.any(concept_scores > 0							,    dim=1				)

						return images, has_nsfw_concepts



