
from __future__ import annotations

lowercase__	:Optional[Any]										=					{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}







class        lowercase	:




		def __init__(       self     ,A__     ,A__):
							lowercase					=		graph
							# mapping node to its parent in resulting breadth first tree
							lowercase					=		{}
							lowercase					=		source_vertex




		def 				A__   (       self):
							lowercase					=		{self.source_vertex}
							lowercase					=		None
							lowercase					=		[self.source_vertex]  # first in first out queue

							while queue:
												lowercase					=		queue.pop(0)
												for adjacent_vertex in self.graph[vertex]:
																	if adjacent_vertex not in visited:
																						visited.add(A__)
																						lowercase					=		vertex
																						queue.append(A__)





		def 				A__   (       self     ,A__):
							if target_vertex == self.source_vertex:
												return self.source_vertex

							lowercase					=		self.parent.get(A__)
							if target_vertex_parent is None:
												lowercase					=		(
												    f'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
												)
												raise ValueError(A__)

							return self.shortest_path(A__) + f'->{target_vertex}'


if __name__ == "__main__":
							lowercase__	:Tuple										=					Graph(graph, "G")
							g.breath_first_search()
							print(g.shortest_path("D"))
							print(g.shortest_path("G"))
							print(g.shortest_path("Foo"))






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




