

'''simple docstring'''





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

from ...utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    CLIPConfig

				lowerCamelCase_		       =    ['CLIPEncoderLayer']







				def __init__(       self     :		Optional[Any]		,					UpperCAmelCase__     :		CLIPConfig   ):
										'''simple docstring'''
										super().__init__(UpperCAmelCase__   )

										lowercase :							List[str]    		=CLIPVisionModelWithProjection(config.vision_config   )

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







				@torch.no_grad()
				def  lowerCamelCase_	(       self     :		Optional[Any]		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]=0.5		,					UpperCAmelCase__     :		int=0.5   ):
										'''simple docstring'''
										lowercase :							str    		=self.vision_model(UpperCAmelCase__   )[0]

										lowercase :							List[str]    		=self.p_head(UpperCAmelCase__   )
										lowercase :							int    		=nsfw_detected.flatten()
										lowercase :							Optional[Any]    		=nsfw_detected > p_threshold
										lowercase :							Union[str, Any]    		=nsfw_detected.tolist()

										if any(UpperCAmelCase__   ):
																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(UpperCAmelCase__   ):
																if nsfw_detected_:
																						lowercase :							Union[str, Any]    		=np.zeros(images[idx].shape   )

										lowercase :							str    		=self.w_head(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=watermark_detected.flatten()
										lowercase :							Tuple    		=watermark_detected > w_threshold
										lowercase :							Union[str, Any]    		=watermark_detected.tolist()

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

										return images, nsfw_detected, watermark_detected








'''simple docstring'''





from collections import defaultdict






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  int:
						lowercase :							Optional[Any]    		=1
						lowercase :							Union[str, Any]    		=True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(__magic_name__						)
						if ret % 2 == 0:
												cuts.append(__magic_name__						)
						return ret






def 				_lowerCAmelCase			(   )				->  int:
						dfs(1						)


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       10, 9
							UpperCamelCase_    						=       defaultdict(list)
							UpperCamelCase_    						=       {}
							UpperCamelCase_    						=       []
							UpperCamelCase_    						=       0
							UpperCamelCase_    						=       [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)





