





"""simple docstring"""


a__    :   Union[str, Any]    						= [
    ["""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 							a__	(     _UpperCAmelCase		)			->     Optional[Any]:

 '''simple docstring'''






 snake_case__										= set()
 for row in edge_array:
  for item in row:
   distinct_edge.add(item[0]		)
 return list(_UpperCAmelCase		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Dict:

 '''simple docstring'''






 snake_case__										= ["0"] * len(_UpperCAmelCase		)
 for i, row in enumerate(_UpperCAmelCase		):
  for item in row:
   if distinct_edge in item[0]:
    snake_case__										= "1"
    break
 return "".join(_UpperCAmelCase		)





def 							a__	(     _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= get_distinct_edge(_UpperCAmelCase		)
 snake_case__										= {}

 for item in distinct_edge:
  snake_case__										= get_bitcode(_UpperCAmelCase		,   _UpperCAmelCase		)
  # print('bit',bit)
  # bt=''.join(bit)
  snake_case__										= bit.count("1"		)
  snake_case__										= [s, bit]
 # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
 snake_case__										= [
     [k, v[0], v[1]]
     for k, v in sorted(frequency_table.items()		,   key=lambda _UpperCAmelCase		: v[1][0]		,   reverse=_UpperCAmelCase		)
 ]
 return sorted_frequency_table





def 							a__	(     _UpperCAmelCase		)			->     List[Any]:

 '''simple docstring'''






 snake_case__										= {}
 for _, item in enumerate(_UpperCAmelCase		):
  nodes.setdefault(item[2]		,   []		).append(item[0]		)
 return nodes





def 							a__	(     _UpperCAmelCase		)			->     List[Any]:

 '''simple docstring'''






 snake_case__										= {}
 for key, value in nodes.items():
  snake_case__										= value
 return cluster





def 							a__	(     _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 return [i * 1_0_0 / len(_UpperCAmelCase		) for i in cluster]





def 							a__	(     )			->     None:

 '''simple docstring'''






 print("\nNodes\n"		)
 for key, value in nodes.items():
  print(_UpperCAmelCase		,   _UpperCAmelCase		)
 print("\nSupport\n"		)
 print(_UpperCAmelCase		)
 print("\n Cluster \n"		)
 for key, value in sorted(cluster.items()		,   reverse=_UpperCAmelCase		):
  print(_UpperCAmelCase		,   _UpperCAmelCase		)
 print("\n Graph\n"		)
 for key, value in graph.items():
  print(_UpperCAmelCase		,   _UpperCAmelCase		)
 print("\n Edge List of Frequent subgraphs \n"		)
 for edge_list in freq_subgraph_edge_list:
  print(_UpperCAmelCase		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     Dict:

 '''simple docstring'''






 for i in cluster[ca]:
  snake_case__										= 0
  snake_case__										= ca + 1
  while ca < max(cluster.keys()		):
   for j in cluster[ca]:
    if int(_UpperCAmelCase		,   2		) & int(_UpperCAmelCase		,   2		) == int(_UpperCAmelCase		,   2		):
     if tuple(nodes[i]		) in graph:
      graph[tuple(nodes[i]		)].append(nodes[j]		)
     else:
      snake_case__										= [nodes[j]]
     count += 1
   if count == 0:
    snake_case__										= ca + 1
   else:
    break





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Union[str, Any]:

 '''simple docstring'''






 snake_case__										= cluster[max(cluster.keys()		)]
 snake_case__										= "Header"
 snake_case__										= {}
 for i in x:
  if (["Header"],) in graph:
   graph[(["Header"],)].append(x[i]		)
  else:
   snake_case__										= [x[i]]
 for i in x:
  snake_case__										= [["Header"]]
 snake_case__										= 1
 while i < max(_UpperCAmelCase		) - 1:
  create_edge(_UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)
  snake_case__										= i + 1
 return graph





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase=None		)			->     int:

 '''simple docstring'''






 snake_case__										= (path or []) + [start]
 if start == end:
  paths.append(_UpperCAmelCase		)
 for node in graph[start]:
  if tuple(_UpperCAmelCase		) not in path:
   my_dfs(_UpperCAmelCase		,   tuple(_UpperCAmelCase		)		,   _UpperCAmelCase		,   _UpperCAmelCase		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 snake_case__										= int(s / 1_0_0 * (len(_UpperCAmelCase		) - 1)		)
 for i in cluster[k]:
  my_dfs(_UpperCAmelCase		,   tuple(cluster[k][i]		)		,   (["Header"],)		)





def 							a__	(     _UpperCAmelCase		)			->     Tuple:

 '''simple docstring'''






 snake_case__										= []
 for edges in paths:
  snake_case__										= []
  for j in range(len(_UpperCAmelCase		) - 1		):
   snake_case__										= list(edges[j]		)
   for e in temp:
    snake_case__										= (e[0], e[1])
    el.append(_UpperCAmelCase		)
  freq_sub_el.append(_UpperCAmelCase		)
 return freq_sub_el





def 							a__	(     _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 for i in range(len(_UpperCAmelCase		)		):
  for j in range(len(edge_array[i]		)		):
   snake_case__										= edge_array[i][j].split("-"		)
   snake_case__										= t


if __name__ == "__main__":
   preprocess(edge_array)
   a__    :   Optional[int]    						= get_frequency_table(edge_array)
   a__    :   List[str]    						= get_nodes(frequency_table)
   a__    :   List[Any]    						= get_cluster(nodes)
   a__    :   Optional[int]    						= get_support(cluster)
   a__    :   Tuple    						= construct_graph(cluster, nodes)
   find_freq_subgraph_given_support(6_0, cluster, graph)
   a__    :   list    						= []
   a__    :   Tuple    						= freq_subgraphs_edge_list(paths)
   print_all()








"""simple docstring"""


from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()

        self.register_buffer("watermark_image"      ,    torch.zeros((62, 62, 4)   )   )
        snake_case__										= None

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= images[0].height
        snake_case__										= images[0].width

        snake_case__										= sample_size or h

        snake_case__										= min(h / sample_size      ,    w / sample_size   )
        snake_case__							,      snake_case__										= (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

        snake_case__							,      snake_case__										= 1024**2, img_w * img_h
        snake_case__										= (Sa / Sa) ** 0.5
        snake_case__							,      snake_case__							,      snake_case__										= int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

        if self.watermark_image_as_pil is None:
         snake_case__										= self.watermark_image.to(torch.uinta   ).cpu().numpy()
         snake_case__										= Image.fromarray(__SCREAMING_SNAKE_CASE      ,    mode="RGBA"   )
         snake_case__										= watermark_image

        snake_case__										= self.watermark_image_as_pil.resize(
            (wm_size, wm_size)      ,    PIL_INTERPOLATION["bicubic"]      ,    reducing_gap=__SCREAMING_SNAKE_CASE   )

        for pil_img in images:
         pil_img.paste(__SCREAMING_SNAKE_CASE      ,    box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)      ,    mask=wm_img.split()[-1]   )

        return images

