code: 
  
  
  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 							_UpperCAmelCase       (UpperCamelCase__  :						Any		):
         _A   : Tuple       					=				set()
         for row in edge_array:
                for item in row:
                       distinct_edge.add(item[0]		)
         return list(UpperCamelCase__		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						int		):
         _A   : Optional[int]       					=				["0"] * len(UpperCamelCase__		)
         for i, row in enumerate(UpperCamelCase__		):
                for item in row:
                       if distinct_edge in item[0]:
                              _A   : Optional[int]       					=				"1"
                              break
         return "".join(UpperCamelCase__		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]		):
         _A   : int       					=				get_distinct_edge(UpperCamelCase__		)
         _A   : List[Any]       					=				{}
  
         for item in distinct_edge:
                _A   : str       					=				get_bitcode(UpperCamelCase__       ,							UpperCamelCase__		)
                # print('bit',bit)
                # bt=''.join(bit)
                _A   : List[str]       					=				bit.count("1"		)
                _A   : Any       					=				[s, bit]
         # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
         _A   : Tuple       					=				[
             [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 							_UpperCAmelCase       (UpperCamelCase__  :						str		):
         _A   : List[Any]       					=				{}
         for _, item in enumerate(UpperCamelCase__		):
                nodes.setdefault(item[2]       ,							[]		).append(item[0]		)
         return nodes
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Tuple		):
         _A   : Optional[Any]       					=				{}
         for key, value in nodes.items():
                _A   : Tuple       					=				value
         return cluster
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Any		):
         return [i * 100 / len(UpperCamelCase__		) for i in cluster]
  def 							_UpperCAmelCase       ():
         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 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Tuple		):
  
  
  
  
  
  
         for i in cluster[ca]:
                _A   : List[Any]       					=				0
                _A   : List[Any]       					=				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:
                                            _A   : List[Any]       					=				[nodes[j]]
                                     count += 1
                       if count == 0:
                              _A   : Optional[int]       					=				ca + 1
                       else:
                              break
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Dict		):
         _A   : List[str]       					=				cluster[max(cluster.keys()		)]
         _A   : int       					=				"Header"
         _A   : List[Any]       					=				{}
         for i in x:
                if (["Header"],) in graph:
                       graph[(["Header"],)].append(x[i]		)
                else:
                       _A   : str       					=				[x[i]]
         for i in x:
                _A   : Dict       					=				[["Header"]]
         _A   : Any       					=				1
         while i < max(UpperCamelCase__		) - 1:
                create_edge(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                _A   : str       					=				i + 1
         return graph
  def 							_UpperCAmelCase       (UpperCamelCase__  :						int       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						List[Any]=None		):
         _A   : Any       					=				(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 							_UpperCAmelCase       (UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
         _A   : str       					=				int(s / 100 * (len(UpperCamelCase__		) - 1)		)
         for i in cluster[k]:
                my_dfs(UpperCamelCase__       ,							tuple(cluster[k][i]		)       ,							(["Header"],)		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Tuple		):
         _A   : int       					=				[]
         for edges in paths:
                _A   : int       					=				[]
                for j in range(len(UpperCamelCase__		) - 1		):
                       _A   : Tuple       					=				list(edges[j]		)
                       for e in temp:
                              _A   : Dict       					=				(e[0], e[1])
                              el.append(UpperCamelCase__		)
                freq_sub_el.append(UpperCamelCase__		)
         return freq_sub_el
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Tuple		):
  
  
  
  
  
  
         for i in range(len(UpperCamelCase__		)		):
                for j in range(len(edge_array[i]		)		):
                       _A   : Union[str, Any]       					=				edge_array[i][j].split("-"		)
                       _A   : List[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()
  
style_context: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
         _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
         _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
         _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data
  
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
         _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
         _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data
  
         _A   : str       					=				4
  
         def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
                _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
                _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
                _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
                _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
  
                if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                       _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                       _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks		):
                _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       if has_attentions:
                              set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                              module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                       set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                       _A   : Optional[int]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
                       module_index += 1
  
         set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
  
         for i, block in enumerate(new_model_architecture.up_blocks		):
                _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                if has_attentions:
                       set_attention_weights(
                           block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                       module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                       _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
  
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
         module_index += 1
         _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				argparse.ArgumentParser()
  
      parser.add_argument(
          '--checkpoint_path',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
          type=str,
          required=False,
          help='Path to the checkpoint to convert.',
      )
  
      parser.add_argument(
          '--config_file',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
          type=str,
          required=False,
          help='The config json file corresponding to the architecture.',
      )
  
      parser.add_argument(
          '--dump_path',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
          type=str,
          required=False,
          help='Path to the output model.',
      )
  
      lowerCAmelCase__   	=				parser.parse_args()
  
      lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')
  
      with open(args.config_file) as f:
          lowerCAmelCase__   	=				json.loads(f.read())
  
      lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )
  
      if "sde" in config:
          del config["sde"]
  
      lowerCAmelCase__   	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      try:
          lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
          lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
          model.save_pretrained(args.dump_path)
  