
from collections.abc import Callable







class        lowercase	:




		def __init__(       self     ,A__ = None):
							# Stores actual heap items.
							lowercase					=		[]
							# Stores indexes of each item for supporting updates and deletion.
							lowercase					=		{}
							# Stores current size of heap.
							lowercase					=		0
							# Stores function used to evaluate the score of an item on which basis ordering
							# will be done.
							lowercase					=		key or (lambda A__: x)




		def 				A__   (       self     ,A__):
							return int((i - 1) / 2) if i > 0 else None




		def 				A__   (       self     ,A__):
							lowercase					=		int(2 * i + 1)
							return left if 0 < left < self.size else None




		def 				A__   (       self     ,A__):
							lowercase					=		int(2 * i + 2)
							return right if 0 < right < self.size else None




		def 				A__   (       self     ,A__     ,A__):
							lowercase			,		lowercase					=		(
							    self.pos_map[self.arr[j][0]],
							    self.pos_map[self.arr[i][0]],
							)
							# Then swap the items in the list.
							lowercase			,		lowercase					=		self.arr[j], self.arr[i]




		def 				A__   (       self     ,A__     ,A__):
							return self.arr[i][1] < self.arr[j][1]




		def 				A__   (       self     ,A__):
							lowercase					=		self._left(A__)
							lowercase					=		self._right(A__)
							lowercase					=		i

							if left is not None and not self._cmp(A__     ,A__):
												lowercase					=		left
							if right is not None and not self._cmp(A__     ,A__):
												lowercase					=		right

							return valid_parent




		def 				A__   (       self     ,A__):
							lowercase					=		self._parent(A__)
							while parent is not None and not self._cmp(A__     ,A__):
												self._swap(A__     ,A__)
												lowercase			,		lowercase					=		parent, self._parent(A__)




		def 				A__   (       self     ,A__):
							lowercase					=		self._get_valid_parent(A__)
							while valid_parent != index:
												self._swap(A__     ,A__)
												lowercase			,		lowercase					=		valid_parent, self._get_valid_parent(A__)




		def 				A__   (       self     ,A__     ,A__):
							if item not in self.pos_map:
												return
							lowercase					=		self.pos_map[item]
							lowercase					=		[item, self.key(A__)]
							# Make sure heap is right in both up and down direction.
							# Ideally only one of them will make any change.
							self._heapify_up(A__)
							self._heapify_down(A__)




		def 				A__   (       self     ,A__):
							if item not in self.pos_map:
												return
							lowercase					=		self.pos_map[item]
							del self.pos_map[item]
							lowercase					=		self.arr[self.size - 1]
							lowercase					=		index
							self.size -= 1
							# Make sure heap is right in both up and down direction. Ideally only one
							# of them will make any change- so no performance loss in calling both.
							if self.size > index:
												self._heapify_up(A__)
												self._heapify_down(A__)




		def 				A__   (       self     ,A__     ,A__):
							lowercase					=		len(self.arr)
							if arr_len == self.size:
												self.arr.append([item, self.key(A__)])
							else:
												lowercase					=		[item, self.key(A__)]
							lowercase					=		self.size
							self.size += 1
							self._heapify_up(self.size - 1)




		def 				A__   (       self):
							return self.arr[0] if self.size else None





		def 				A__   (       self):
							lowercase					=		self.get_top()
							if top_item_tuple:
												self.delete_item(top_item_tuple[0])
							return top_item_tuple



def  UpperCamelCase					(							):
					'''simple docstring'''


if __name__ == "__main__":
							import doctest

							doctest.testmod()






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

from ...utils import logging


lowercase__	:Union[str, Any]										=					logging.get_logger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Union[str, Any]       =CLIPConfig

							lowercase_ : str       =['''CLIPEncoderLayer''']




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

												lowercase					=		CLIPVisionModelWithProjection(config.vision_config)

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





							@torch.no_grad()
							def 				A__   (       self     ,A__     ,A__     ,A__=0.5     ,A__=0.5):
												lowercase					=		self.vision_model(A__)[0]

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

												if any(A__):
																	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(A__):
																	if nsfw_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

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

												if any(A__):
																	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(A__):
																	if watermark_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												return images, nsfw_detected, watermark_detected




