


snake_case__									= [
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12', 'cd-e2', 'ce-e4',
     'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3', 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3'],
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'cd-e2', 'de-e1', 'df-e8',
     'ef-e3', 'eg-e2', 'fg-e6'],
    ['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'de-e1', 'df-e8', 'dg-e5', 'ef-e3', 'eg-e2',
     'eh-e12', 'fg-e6', 'fh-e10', 'gh-e6'],
    ['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',
     'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']
]
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]				)       ->  str:
						__magic_name__				:							Optional[int]				=      set()
						for row in edge_array:
												for item in row:
																		distinct_edge.add(item[0]				)
						return list(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :Tuple				)       ->  int:
						__magic_name__				:							Union[str, Any]				=      ['0'] * len(lowercase__				)
						for i, row in enumerate(lowercase__				):
												for item in row:
																		if distinct_edge in item[0]:
																								__magic_name__				:							Optional[int]				=      '1'
																								break
						return "".join(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int				)       ->  Tuple:
						__magic_name__				:							Dict				=      get_distinct_edge(lowercase__				)
						__magic_name__				:							List[str]				=      {}

						for item in distinct_edge:
												__magic_name__				:							str				=      get_bitcode(lowercase__      ,						lowercase__				)
												# print('bit',bit)
												# bt=''.join(bit)
												__magic_name__				:							Optional[Any]				=      bit.count('1'				)
												__magic_name__				:							Optional[int]				=      [s, bit]
						# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
						__magic_name__				:							Optional[int]				=      [
						    [k, v[0], v[1]]
						    for k, v in sorted(frequency_table.items()      ,						key=lambda lowercase__				: v[1][0]      ,						reverse=lowercase__				)
						]
						return sorted_frequency_table
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]				)       ->  Tuple:
						__magic_name__				:							int				=      {}
						for _, item in enumerate(lowercase__				):
												nodes.setdefault(item[2]      ,						[]				).append(item[0]				)
						return nodes
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]				)       ->  Optional[int]:
						__magic_name__				:							List[Any]				=      {}
						for key, value in nodes.items():
												__magic_name__				:							int				=      value
						return cluster
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any				)       ->  Optional[Any]:
						return [i * 1_0_0 / len(lowercase__				) for i in cluster]
def  __SCREAMING_SNAKE_CASE       (				)       ->  None:
						print('\nNodes\n'				)
						for key, value in nodes.items():
												print(lowercase__      ,						lowercase__				)
						print('\nSupport\n'				)
						print(lowercase__				)
						print('\n Cluster \n'				)
						for key, value in sorted(cluster.items()      ,						reverse=lowercase__				):
												print(lowercase__      ,						lowercase__				)
						print('\n Graph\n'				)
						for key, value in graph.items():
												print(lowercase__      ,						lowercase__				)
						print('\n Edge List of Frequent subgraphs \n'				)
						for edge_list in freq_subgraph_edge_list:
												print(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :Any      ,						lowercase__  :int      ,						lowercase__  :Any				)       ->  List[str]:
						for i in cluster[ca]:
												__magic_name__				:							Optional[int]				=      0
												__magic_name__				:							Dict				=      ca + 1
												while ca < max(cluster.keys()				):
																		for j in cluster[ca]:
																								if int(lowercase__      ,						2				) & int(lowercase__      ,						2				) == int(lowercase__      ,						2				):
																														if tuple(nodes[i]				) in graph:
																																				graph[tuple(nodes[i]				)].append(nodes[j]				)
																														else:
																																				__magic_name__				:							Any				=      [nodes[j]]
																														count += 1
																		if count == 0:
																								__magic_name__				:							List[Any]				=      ca + 1
																		else:
																								break
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Optional[int]				)       ->  int:
						__magic_name__				:							Union[str, Any]				=      cluster[max(cluster.keys()				)]
						__magic_name__				:							Dict				=      'Header'
						__magic_name__				:							Tuple				=      {}
						for i in x:
												if (["Header"],) in graph:
																		graph[(["Header"],)].append(x[i]				)
												else:
																		__magic_name__				:							Union[str, Any]				=      [x[i]]
						for i in x:
												__magic_name__				:							Optional[Any]				=      [['Header']]
						__magic_name__				:							str				=      1
						while i < max(lowercase__				) - 1:
												create_edge(lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__				)
												__magic_name__				:							Dict				=      i + 1
						return graph
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :Dict=None				)       ->  Any:
						__magic_name__				:							List[Any]				=      (path or []) + [start]
						if start == end:
												paths.append(lowercase__				)
						for node in graph[start]:
												if tuple(lowercase__				) not in path:
																		my_dfs(lowercase__      ,						tuple(lowercase__				)      ,						lowercase__      ,						lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :Optional[int]      ,						lowercase__  :Union[str, Any]				)       ->  int:
						__magic_name__				:							Dict				=      int(s / 1_0_0 * (len(lowercase__				) - 1)				)
						for i in cluster[k]:
												my_dfs(lowercase__      ,						tuple(cluster[k][i]				)      ,						(['Header'],)				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict				)       ->  Union[str, Any]:
						__magic_name__				:							Union[str, Any]				=      []
						for edges in paths:
												__magic_name__				:							Dict				=      []
												for j in range(len(lowercase__				) - 1				):
																		__magic_name__				:							Dict				=      list(edges[j]				)
																		for e in temp:
																								__magic_name__				:							int				=      (e[0], e[1])
																								el.append(lowercase__				)
												freq_sub_el.append(lowercase__				)
						return freq_sub_el
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict				)       ->  List[Any]:
						for i in range(len(lowercase__				)				):
												for j in range(len(edge_array[i]				)				):
																		__magic_name__				:							Optional[int]				=      edge_array[i][j].split('-'				)
																		__magic_name__				:							List[Any]				=      t


if __name__ == "__main__":
			preprocess(edge_array)
			snake_case__									= get_frequency_table(edge_array)
			snake_case__									= get_nodes(frequency_table)
			snake_case__									= get_cluster(nodes)
			snake_case__									= get_support(cluster)
			snake_case__									= construct_graph(cluster, nodes)
			find_freq_subgraph_given_support(60, cluster, graph)
			snake_case__									= []
			snake_case__									= freq_subgraphs_edge_list(paths)
			print_all()






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

from ...utils import logging


snake_case__     							= logging.get_logger(__name__)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :List[str]				)       ->  Tuple:
						__magic_name__				:							List[str]				=      nn.functional.normalize(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      nn.functional.normalize(lowercase__				)
						return torch.mm(lowercase__      ,						normalized_text_embeds.t()				)



class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      CLIPConfig

				__lowerCAmelCase		=      ['''CLIPEncoderLayer''']
				def __init__(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										super().__init__(_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      CLIPVisionModel(config.vision_config      )
										__magic_name__				:							Tuple				=      nn.Linear(config.vision_config.hidden_size     ,      config.projection_dim     ,      bias=_UpperCAmelCase      )

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

										__magic_name__				:							Optional[Any]				=      nn.Parameter(torch.ones(17      )     ,      requires_grad=_UpperCAmelCase      )
										__magic_name__				:							str				=      nn.Parameter(torch.ones(3      )     ,      requires_grad=_UpperCAmelCase      )
				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.vision_model(_UpperCAmelCase      )[1]  # pooled_output
										__magic_name__				:							Any				=      self.visual_projection(_UpperCAmelCase      )

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

										__magic_name__				:							str				=      []
										__magic_name__				:							List[Any]				=      image_embeds.shape[0]
										for i in range(_UpperCAmelCase      ):
																__magic_name__				:							Optional[Any]				=      {'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
																__magic_name__				:							Optional[int]				=      0.0

																for concept_idx in range(len(special_cos_dist[0]      )      ):
																						__magic_name__				:							Any				=      special_cos_dist[i][concept_idx]
																						__magic_name__				:							Tuple				=      self.special_care_embeds_weights[concept_idx].item()
																						__magic_name__				:							Dict				=      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]}      )
																												__magic_name__				:							str				=      0.01

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

																result.append(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      [len(res['bad_concepts']      ) > 0 for res in result]

										for idx, has_nsfw_concept in enumerate(_UpperCAmelCase      ):
																if has_nsfw_concept:
																						if torch.is_tensor(_UpperCAmelCase      ) or torch.is_tensor(images[0]      ):
																												__magic_name__				:							Union[str, Any]				=      torch.zeros_like(images[idx]      )  # black image
																						else:
																												__magic_name__				:							Optional[int]				=      np.zeros(images[idx].shape      )  # black image

										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.'      )

										return images, has_nsfw_concepts





				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.vision_model(_UpperCAmelCase      )[1]  # pooled_output
										__magic_name__				:							List[str]				=      self.visual_projection(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      cosine_distance(_UpperCAmelCase     ,      self.special_care_embeds      )
										__magic_name__				:							Optional[int]				=      cosine_distance(_UpperCAmelCase     ,      self.concept_embeds      )

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

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

										__magic_name__				:							Optional[int]				=      (cos_dist - self.concept_embeds_weights) + special_adjustment
										# concept_scores = concept_scores.round(decimals=3)
										__magic_name__				:							Union[str, Any]				=      torch.any(concept_scores > 0     ,      dim=1      )

										__magic_name__				:							Dict				=      0.0  # black image

										return images, has_nsfw_concepts


