

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   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	set()
  for row in edge_array:
    for item in row:
      distinct_edge.add(item[0]      )
  return list(__A      )
def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	["0"] * len(__A      )
  for i, row in enumerate(__A      ):
    for item in row:
      if distinct_edge in item[0]:
        UpperCAmelCase          =	"1"
        break
  return "".join(__A      )
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	get_distinct_edge(__A      )
  UpperCAmelCase          =	{}

  for item in distinct_edge:
    UpperCAmelCase          =	get_bitcode(__A ,							__A      )
    # print('bit',bit)
    # bt=''.join(bit)
    UpperCAmelCase          =	bit.count("1"      )
    UpperCAmelCase          =	[s, bit]
  # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
  UpperCAmelCase          =	[
      [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      ):
  UpperCAmelCase          =	{}
  for _, item in enumerate(__A      ):
    nodes.setdefault(item[2] ,							[]      ).append(item[0]      )
  return nodes
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	{}
  for key, value in nodes.items():
    UpperCAmelCase          =	value
  return cluster
def   _lowerCAmelCase(						__A      ):
  return [i * 100 / len(__A      ) for i in cluster]
def   _lowerCAmelCase(						):
  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      ):
  for i in cluster[ca]:
    UpperCAmelCase          =	0
    UpperCAmelCase          =	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:
            UpperCAmelCase          =	[nodes[j]]
          count += 1
      if count == 0:
        UpperCAmelCase          =	ca + 1
      else:
        break
def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	cluster[max(cluster.keys()      )]
  UpperCAmelCase          =	"Header"
  UpperCAmelCase          =	{}
  for i in x:
    if (["Header"],) in graph:
      graph[(["Header"],)].append(x[i]      )
    else:
      UpperCAmelCase          =	[x[i]]
  for i in x:
    UpperCAmelCase          =	[["Header"]]
  UpperCAmelCase          =	1
  while i < max(__A      ) - 1:
    create_edge(__A ,							__A ,							__A ,							__A      )
    UpperCAmelCase          =	i + 1
  return graph
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A=None      ):
  UpperCAmelCase          =	(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      ):
  UpperCAmelCase          =	int(s / 100 * (len(__A      ) - 1)      )
  for i in cluster[k]:
    my_dfs(__A ,							tuple(cluster[k][i]      ) ,							(["Header"],)      )
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	[]
  for edges in paths:
    UpperCAmelCase          =	[]
    for j in range(len(__A      ) - 1      ):
      UpperCAmelCase          =	list(edges[j]      )
      for e in temp:
        UpperCAmelCase          =	(e[0], e[1])
        el.append(__A      )
    freq_sub_el.append(__A      )
  return freq_sub_el
def   _lowerCAmelCase(						__A      ):
  for i in range(len(__A      )      ):
    for j in range(len(edge_array[i]      )      ):
      UpperCAmelCase          =	edge_array[i][j].split("-"      )
      UpperCAmelCase          =	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()






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A=None      ):
  UpperCAmelCase          =	None
  # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
  if str(weight.dtype      ) == "torch.bfloat16":
    # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
    UpperCAmelCase          =	weight.view(torch.intaa      )
    UpperCAmelCase          =	"bfloat16"
  UpperCAmelCase          =	weight.cpu().numpy()
  UpperCAmelCase          =	os.path.join(__A ,							F"{weight_name}.dat"      )
  if index is not None:
    if dtype is None:
      UpperCAmelCase          =	str(array.dtype      )
    UpperCAmelCase          =	{"dtype": dtype, "shape": list(array.shape      )}
  if array.ndim == 0:
    UpperCAmelCase          =	array[None]
  UpperCAmelCase          =	np.memmap(__A ,							dtype=array.dtype ,							mode="w+" ,							shape=array.shape      )
  UpperCAmelCase          =	array[:]
  file_array.flush()
  return index
def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	tuple(weight_info["shape"]      )
  if shape == ():
    # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
    UpperCAmelCase          =	(1,)

  UpperCAmelCase          =	weight_info["dtype"]
  if dtype == "bfloat16":
    # NumPy does not support bfloat16 so this was saved as a int16
    UpperCAmelCase          =	"int16"

  UpperCAmelCase          =	np.memmap(__A ,							dtype=__A ,							shape=__A ,							mode="r"      )

  if len(weight_info["shape"]      ) == 0:
    UpperCAmelCase          =	weight[0]
  UpperCAmelCase          =	torch.tensor(__A      )
  if weight_info["dtype"] == "bfloat16":
    UpperCAmelCase          =	weight.view(torch.bfloataa      )

  return weight
def   _lowerCAmelCase(						__A ,							__A      ):
  if index is None or len(__A      ) == 0:
    # Nothing to save
    return

  UpperCAmelCase          =	os.path.join(__A ,							"index.json"      )
  if os.path.isfile(__A      ):
    with open(__A ,							"r" ,							encoding="utf-8"      ) as f:
      UpperCAmelCase          =	json.load(__A      )
  else:
    UpperCAmelCase          =	{}
  current_index.update(__A      )

  with open(__A ,							"w" ,							encoding="utf-8"      ) as f:
    json.dump(__A ,							__A ,							indent=2      )
def   _lowerCAmelCase(						__A ,							__A      ):
  os.makedirs(__A ,							exist_ok=__A      )
  UpperCAmelCase          =	{}
  for name, parameter in state_dict.items():
    UpperCAmelCase          =	offload_weight(__A ,							__A ,							__A ,							index=__A      )

  # Update index
  save_offload_index(__A ,							__A      )






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			Tuple      ,    lowerCAmelCase__		:			Mapping      ,    lowerCAmelCase__		:			str     )     ->   Tuple:
   UpperCAmelCase          =	dataset
   UpperCAmelCase          =	prefix


 def __getitem__(    self		:			Dict      ,    lowerCAmelCase__		:			Any     )     ->   Optional[int]:
   return self.dataset[f"{self.prefix}{key}"]


 def __iter__(    self		:			Optional[int]     )     ->   Any:
   return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )


 def __len__(    self		:			Union[str, Any]     )     ->   int:
   return len(self.dataset     )






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			Dict[str, torch.Tensor] = None      ,    lowerCAmelCase__		:			Optional[Union[str, os.PathLike]] = None      ,    lowerCAmelCase__		:			Mapping = None      ,    lowerCAmelCase__		:			Dict=None      ,    )     ->   Dict:
   if state_dict is None and save_folder is None:
     raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."     )

   UpperCAmelCase          =	{} if state_dict is None else state_dict
   UpperCAmelCase          =	save_folder
   if index is None and save_folder is not None:
     with open(os.path.join(lowerCAmelCase__      ,    "index.json"     )     ) as f:
       UpperCAmelCase          =	json.load(lowerCAmelCase__     )
   UpperCAmelCase          =	{} if index is None else index
   UpperCAmelCase          =	list(self.state_dict.keys()     )
   self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
   UpperCAmelCase          =	device


 def __getitem__(    self		:			str      ,    lowerCAmelCase__		:			str     )     ->   Dict:
   # State dict gets priority
   if key in self.state_dict:
     return self.state_dict[key]
   UpperCAmelCase          =	self.index[key]
   if weight_info.get("safetensors_file"     ) is not None:
     if not is_safetensors_available():
       raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`."     )

     from safetensors import safe_open

     UpperCAmelCase          =	"cpu" if self.device is None else self.device
     with safe_open(weight_info["safetensors_file"]      ,    framework="pt"      ,    device=lowerCAmelCase__     ) as f:
       UpperCAmelCase          =	f.get_tensor(weight_info.get("weight_name"      ,    lowerCAmelCase__     )     )

     if "dtype" in weight_info:
       return tensor.to(getattr(lowerCAmelCase__      ,    weight_info["dtype"]     )     )
     else:
       return tensor

   UpperCAmelCase          =	os.path.join(self.save_folder      ,    f"{key}.dat"     )
   return load_offloaded_weight(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def __iter__(    self		:			str     )     ->   Dict:
   return iter(self.all_keys     )


 def __len__(    self		:			Optional[Any]     )     ->   List[Any]:
   return len(self.all_keys     )

def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	{}
  for module_name in submodule_names:
    # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
    # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
    result.update(
        {
            key: param
            for key, param in state_dict.items()
            if key == module_name or key.startswith(module_name + "."      )
        }      )
  return result



