
'''simple docstring'''
A__ :		List[str]					=[
    ['''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_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							Union[str, Any]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		set()
  for row in edge_array:
    for item in row:
      distinct_edge.add(item[0]			)
  return list(_A			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[str]			)					->							str:





  """simple docstring"""

  __A		:  List[str]       				=		["""0"""] * len(_A			)
  for i, row in enumerate(_A			):
    for item in row:
      if distinct_edge in item[0]:
        __A		:  List[Any]       				=		"""1"""
        break
  return "".join(_A			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]			)					->							int:





  """simple docstring"""

  __A		:  str       				=		get_distinct_edge(_A			)
  __A		:  Union[str, Any]       				=		{}

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]			)					->							Optional[Any]:





  """simple docstring"""

  __A		:  List[str]       				=		{}
  for _, item in enumerate(_A			):
    nodes.setdefault(item[2]      ,  []			).append(item[0]			)
  return nodes







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]			)					->							int:





  """simple docstring"""

  __A		:  Optional[int]       				=		{}
  for key, value in nodes.items():
    __A		:  int       				=		value
  return cluster







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Any			)					->							Any:





  """simple docstring"""

  return [i * 100 / len(_A			) for i in cluster]







def 							A_  (    )					->							str:





  """simple docstring"""

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	Dict			)					->							List[str]:





  """simple docstring"""

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]			)					->							Union[str, Any]:





  """simple docstring"""

  __A		:  List[str]       				=		cluster[max(cluster.keys()			)]
  __A		:  Dict       				=		"""Header"""
  __A		:  Tuple       				=		{}
  for i in x:
    if (["Header"],) in graph:
      graph[(["Header"],)].append(x[i]			)
    else:
      __A		:  Optional[int]       				=		[x[i]]
  for i in x:
    __A		:  str       				=		[["""Header"""]]
  __A		:  int       				=		1
  while i < max(_A			) - 1:
    create_edge(_A      ,  _A      ,  _A      ,  _A			)
    __A		:  Any       				=		i + 1
  return graph







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	List[str]=None			)					->							List[str]:





  """simple docstring"""

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	List[Any]			)					->							Optional[int]:





  """simple docstring"""

  __A		:  str       				=		int(s / 100 * (len(_A			) - 1)			)
  for i in cluster[k]:
    my_dfs(_A      ,  tuple(cluster[k][i]			)      ,  (["""Header"""],)			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							List[Any]:





  """simple docstring"""

  __A		:  Any       				=		[]
  for edges in paths:
    __A		:  Tuple       				=		[]
    for j in range(len(_A			) - 1			):
      __A		:  List[Any]       				=		list(edges[j]			)
      for e in temp:
        __A		:  Dict       				=		(e[0], e[1])
        el.append(_A			)
    freq_sub_el.append(_A			)
  return freq_sub_el







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]			)					->							Tuple:





  """simple docstring"""

  for i in range(len(_A			)			):
    for j in range(len(edge_array[i]			)			):
      __A		:  int       				=		edge_array[i][j].split("""-"""			)
      __A		:  Optional[Any]       				=		t


if __name__ == "__main__":
     preprocess(edge_array)
     A__ :		int					=get_frequency_table(edge_array)
     A__ :		Optional[int]					=get_nodes(frequency_table)
     A__ :		List[str]					=get_cluster(nodes)
     A__ :		Dict					=get_support(cluster)
     A__ :		str					=construct_graph(cluster, nodes)
     find_freq_subgraph_given_support(60, cluster, graph)
     A__ :		list					=[]
     A__ :		List[Any]					=freq_subgraphs_edge_list(paths)
     print_all()











'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
