



'''simple docstring'''





lowercase_			=  [
    ["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 lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



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




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  get_distinct_edge(__A)
     _a     =  {}

     for item in distinct_edge:
          _a     =  get_bitcode(__A  ,     __A)
          # 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 __A: v[1][0]  ,     reverse=__A)
     ]
     return sorted_frequency_table




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  ():



     """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 lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A):



     """simple docstring"""



     for i in cluster[ca]:
          _a     =  0
          _a     =  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     =  [nodes[j]]
                         count += 1
               if count == 0:
                    _a     =  ca + 1
               else:
                    break




def lowerCAmelCase  (__A  ,     __A):



     """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(__A) - 1:
          create_edge(__A  ,     __A  ,     __A  ,     __A)
          _a     =  i + 1
     return graph




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A=None):



     """simple docstring"""



     _a     =  (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 lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



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




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A):



     """simple docstring"""



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


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








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_			=  {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  ["SegformerFeatureExtractor"]
  lowercase_			=  ["SegformerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SegformerDecodeHead",
      "SegformerForImageClassification",
      "SegformerForSemanticSegmentation",
      "SegformerLayer",
      "SegformerModel",
      "SegformerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSegformerDecodeHead",
      "TFSegformerForImageClassification",
      "TFSegformerForSemanticSegmentation",
      "TFSegformerModel",
      "TFSegformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_segformer import SegformerFeatureExtractor
    from .image_processing_segformer import SegformerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_segformer import (
        SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SegformerDecodeHead,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerLayer,
        SegformerModel,
        SegformerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_segformer import (
        TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSegformerDecodeHead,
        TFSegformerForImageClassification,
        TFSegformerForSemanticSegmentation,
        TFSegformerModel,
        TFSegformerPreTrainedModel,
    )

else:
  import sys

  lowercase_			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



