





snake_case__      :							Dict									=  [
    ['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       (			lowerCamelCase:     int				)			->		str:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     str      ,       lowerCamelCase:     Dict				)			->		Optional[Any]:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     List[Any]				)			->		Optional[Any]:







      '''simple docstring'''






      __A      =       get_distinct_edge(lowerCamelCase				)
      __A      =       {}

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



def _a       (			lowerCamelCase:     Any				)			->		Optional[Any]:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     int				)			->		Tuple:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      return [i * 1_00 / len(lowerCamelCase				) for i in cluster]



def _a       (			)			->		None:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     Optional[Any]      ,       lowerCamelCase:     Optional[int]      ,       lowerCamelCase:     str      ,       lowerCamelCase:     List[str]				)			->		List[Any]:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     Union[str, Any]				)			->		Union[str, Any]:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     List[str]      ,       lowerCamelCase:     Dict      ,       lowerCamelCase:     Optional[Any]      ,       lowerCamelCase:     Tuple=None				)			->		Tuple:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     Union[str, Any]      ,       lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		List[Any]:







      '''simple docstring'''






      __A      =       int(s / 1_00 * (len(lowerCamelCase				) - 1)				)
      for i in cluster[k]:
            my_dfs(lowerCamelCase      ,       tuple(cluster[k][i]				)      ,       (['''Header'''],)				)



def _a       (			lowerCamelCase:     Tuple				)			->		List[str]:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     List[Any]				)			->		Union[str, Any]:







      '''simple docstring'''






      for i in range(len(lowerCamelCase				)				):
            for j in range(len(edge_array[i]				)				):
                  __A      =       edge_array[i][j].split('''-'''				)
                  __A      =       t


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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case__      :							Optional[int]									=  {'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__      :							Union[str, Any]									=  [
           'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CvtForImageClassification',
           'CvtModel',
           'CvtPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__      :							List[str]									=  [
           'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCvtForImageClassification',
           'TFCvtModel',
           'TFCvtPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_cvt import (
                  CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CvtForImageClassification,
                  CvtModel,
                  CvtPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_cvt import (
                  TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCvtForImageClassification,
                  TFCvtModel,
                  TFCvtPreTrainedModel,
              )


else:
       import sys

       snake_case__      :							Dict									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





