
from __future__ import annotations

from random import random




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase = None					)							->			List[str]:
					lowerCamelCase_         =     value
					lowerCamelCase_         =     random()
					lowerCamelCase_         =     None
					lowerCamelCase_         =     None



	def __repr__(       self					)							->			str:
					from pprint import pformat

					if self.left is None and self.right is None:
									return f'\'{self.value}: {self.prior:.5}\''
					else:
									return pformat(
									    {f'{self.value}: {self.prior:.5}': (self.left, self.right)}		,    indent=1					)



	def __str__(       self					)							->			str:
					lowerCamelCase_         =     str(self.value					) + " "
					lowerCamelCase_         =     str(self.left or ""					)
					lowerCamelCase_         =     str(self.right or ""					)
					return value + left + right





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				if root is None:  # None tree is split into 2 Nones
								return None, None
				elif root.value is None:
								return None, None
				else:
								if value < root.value:
												lowerCamelCase_      ,       lowerCamelCase_         =     split(root.left      ,					lowerCamelCase__       )
												return left, root
								else:
												lowerCamelCase_      ,       lowerCamelCase_         =     split(root.right      ,					lowerCamelCase__       )
												return root, right





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				if (not left) or (not right):  # If one node is None, return the other
								return left or right
				elif left.prior < right.prior:
								lowerCamelCase_         =     merge(left.right      ,					lowerCamelCase__       )
								return left
				else:
								lowerCamelCase_         =     merge(lowerCamelCase__      ,					right.left       )
								return right





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     Node(lowerCamelCase__       )
				lowerCamelCase_      ,       lowerCamelCase_         =     split(lowerCamelCase__      ,					lowerCamelCase__       )
				return merge(merge(lowerCamelCase__      ,					lowerCamelCase__       )      ,					lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_      ,       lowerCamelCase_         =     split(lowerCamelCase__      ,					value - 1       )
				lowerCamelCase_      ,       lowerCamelCase_         =     split(lowerCamelCase__      ,					lowerCamelCase__       )
				return merge(lowerCamelCase__      ,					lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not root:  # None
								return
				else:
								inorder(root.left       )
								print(root.value      ,					end=","       )
								inorder(root.right       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				for arg in args.split():
								if arg[0] == "+":
												lowerCamelCase_         =     insert(lowerCamelCase__      ,					int(arg[1:]       )       )

								elif arg[0] == "-":
												lowerCamelCase_         =     erase(lowerCamelCase__      ,					int(arg[1:]       )       )

								else:
												print("Unknown command"       )

				return root





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     None
				print(
				    "enter numbers to create a tree, + value to add value into treap, "
				    "- value to erase all nodes with value. 'q' to quit. "       )

				lowerCamelCase_         =     input()
				while args != "q":
								lowerCamelCase_         =     interact_treap(lowerCamelCase__      ,					lowerCamelCase__       )
								print(lowerCamelCase__       )
								lowerCamelCase_         =     input()

				print("good by!"       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()




import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


__A							=logging.get_logger(__name__)




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					CLIPConfig

	lowerCAmelCase__   =					['CLIPEncoderLayer']



	def __init__(       self		,    lowercase					)							->			int:
					super().__init__(lowercase					)

					lowerCamelCase_         =     CLIPVisionModelWithProjection(config.vision_config					)

					lowerCamelCase_         =     nn.Linear(config.vision_config.projection_dim		,    1					)
					lowerCamelCase_         =     nn.Linear(config.vision_config.projection_dim		,    1					)



	@torch.no_grad()
	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase=0.5		,    lowercase=0.5					)							->			int:
					lowerCamelCase_         =     self.vision_model(lowercase					)[0]

					lowerCamelCase_         =     self.p_head(lowercase					)
					lowerCamelCase_         =     nsfw_detected.flatten()
					lowerCamelCase_         =     nsfw_detected > p_threshold
					lowerCamelCase_         =     nsfw_detected.tolist()

					if any(lowercase					):
									logger.warning(
									    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
									    " Try again with a different prompt and/or seed."					)

					for idx, nsfw_detected_ in enumerate(lowercase					):
									if nsfw_detected_:
													lowerCamelCase_         =     np.zeros(images[idx].shape					)

					lowerCamelCase_         =     self.w_head(lowercase					)
					lowerCamelCase_         =     watermark_detected.flatten()
					lowerCamelCase_         =     watermark_detected > w_threshold
					lowerCamelCase_         =     watermark_detected.tolist()

					if any(lowercase					):
									logger.warning(
									    "Potential watermarked content was detected in one or more images. A black image will be returned instead."
									    " Try again with a different prompt and/or seed."					)

					for idx, watermark_detected_ in enumerate(lowercase					):
									if watermark_detected_:
													lowerCamelCase_         =     np.zeros(images[idx].shape					)

					return images, nsfw_detected, watermark_detected


