


'''simple docstring'''





__lowerCamelCase       				=	[
    ['''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_	:			List[str]	):
   UpperCAmelCase__  :str            =			set()
   for row in edge_array:
      for item in row:
         distinct_edge.add(item[0]	)
   return list(UpperCamelCase_	)


def 					a__    (				UpperCamelCase_	:			Tuple,      UpperCamelCase_	:			List[Any]	):
   UpperCAmelCase__  :int            =			['''0'''] * len(UpperCamelCase_	)
   for i, row in enumerate(UpperCamelCase_	):
      for item in row:
         if distinct_edge in item[0]:
            UpperCAmelCase__  :List[Any]            =			'''1'''
            break
   return "".join(UpperCamelCase_	)


def 					a__    (				UpperCamelCase_	:			Optional[int]	):
   UpperCAmelCase__  :Any            =			get_distinct_edge(UpperCamelCase_	)
   UpperCAmelCase__  :Dict            =			{}

   for item in distinct_edge:
      UpperCAmelCase__  :List[str]            =			get_bitcode(UpperCamelCase_,      UpperCamelCase_	)
      # print('bit',bit)
      # bt=''.join(bit)
      UpperCAmelCase__  :int            =			bit.count('''1'''	)
      UpperCAmelCase__  :Tuple            =			[s, bit]
   # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
   UpperCAmelCase__  :str            =			[
       [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_	:			Optional[int]	):
   UpperCAmelCase__  :int            =			{}
   for _, item in enumerate(UpperCamelCase_	):
      nodes.setdefault(item[2],      []	).append(item[0]	)
   return nodes


def 					a__    (				UpperCamelCase_	:			Any	):
   UpperCAmelCase__  :Any            =			{}
   for key, value in nodes.items():
      UpperCAmelCase__  :Optional[int]            =			value
   return cluster


def 					a__    (				UpperCamelCase_	:			Tuple	):
   return [i * 100 / len(UpperCamelCase_	) for i in cluster]


def 					a__    (				):
   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_	:			Optional[int],      UpperCamelCase_	:			Any,      UpperCamelCase_	:			int,      UpperCamelCase_	:			Dict	):
   for i in cluster[ca]:
      UpperCAmelCase__  :int            =			0
      UpperCAmelCase__  :int            =			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:
                  UpperCAmelCase__  :int            =			[nodes[j]]
               count += 1
         if count == 0:
            UpperCAmelCase__  :List[Any]            =			ca + 1
         else:
            break


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


def 					a__    (				UpperCamelCase_	:			Optional[Any],      UpperCamelCase_	:			int,      UpperCamelCase_	:			Any,      UpperCamelCase_	:			Dict=None	):
   UpperCAmelCase__  :Tuple            =			(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_	:			Dict,      UpperCamelCase_	:			int,      UpperCamelCase_	:			List[Any]	):
   UpperCAmelCase__  :List[str]            =			int(s / 100 * (len(UpperCamelCase_	) - 1)	)
   for i in cluster[k]:
      my_dfs(UpperCamelCase_,      tuple(cluster[k][i]	),      (['''Header'''],)	)


def 					a__    (				UpperCamelCase_	:			List[Any]	):
   UpperCAmelCase__  :int            =			[]
   for edges in paths:
      UpperCAmelCase__  :List[Any]            =			[]
      for j in range(len(UpperCamelCase_	) - 1	):
         UpperCAmelCase__  :Any            =			list(edges[j]	)
         for e in temp:
            UpperCAmelCase__  :str            =			(e[0], e[1])
            el.append(UpperCamelCase_	)
      freq_sub_el.append(UpperCamelCase_	)
   return freq_sub_el


def 					a__    (				UpperCamelCase_	:			Dict	):
   for i in range(len(UpperCamelCase_	)	):
      for j in range(len(edge_array[i]	)	):
         UpperCAmelCase__  :Dict            =			edge_array[i][j].split('''-'''	)
         UpperCAmelCase__  :Optional[Any]            =			t


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







'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

__lowerCamelCase       				=	logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


    def 					a__    (				cls	:			Optional[int]	):
       return cls


def 					a__    (				UpperCamelCase_	:			Union[Tuple, List],      UpperCamelCase_	:			Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,      UpperCamelCase_	:			Optional["torch.device"] = None,      UpperCamelCase_	:			Optional["torch.dtype"] = None,      UpperCamelCase_	:			Optional["torch.layout"] = None,      ):
   UpperCAmelCase__  :Union[str, Any]            =			device
   UpperCAmelCase__  :List[Any]            =			shape[0]

   UpperCAmelCase__  :Tuple            =			layout or torch.strided
   UpperCAmelCase__  :Union[str, Any]            =			device or torch.device('''cpu'''	)

   if generator is not None:
      UpperCAmelCase__  :str            =			generator.device.type if not isinstance(UpperCamelCase_,      UpperCamelCase_	) else generator[0].device.type
      if gen_device_type != device.type and gen_device_type == "cpu":
         UpperCAmelCase__  :Tuple            =			'''cpu'''
         if device != "mps":
            logger.info(
                F'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
                F''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
                F''' slighly speed up this function by passing a generator that was created on the {device} device.'''	)
      elif gen_device_type != device.type and gen_device_type == "cuda":
         raise ValueError(F'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''	)

   if isinstance(UpperCamelCase_,      UpperCamelCase_	):
      UpperCAmelCase__  :List[str]            =			(1,) + shape[1:]
      UpperCAmelCase__  :Optional[int]            =			[
          torch.randn(UpperCamelCase_,      generator=generator[i],      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	)
          for i in range(UpperCamelCase_	)
      ]
      UpperCAmelCase__  :int            =			torch.cat(UpperCamelCase_,      dim=0	).to(UpperCamelCase_	)
   else:
      UpperCAmelCase__  :Optional[int]            =			torch.randn(UpperCamelCase_,      generator=UpperCamelCase_,      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	).to(UpperCamelCase_	)

   return latents


def 					a__    (				UpperCamelCase_	:			Dict	):
   if is_torch_version('''<''',      '''2.0.0'''	) or not hasattr(UpperCamelCase_,      '''_dynamo'''	):
      return False
   return isinstance(UpperCamelCase_,      torch._dynamo.eval_frame.OptimizedModule	)



