code: 
  
  
  
  
  SCREAMING_SNAKE_CASE       = [
      ["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 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
   A__ =       set()
   for row in edge_array:
    for item in row:
     distinct_edge.add(item[0]		)
   return list(lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Dict:
   A__ =       ["0"] * len(lowercase_		)
   for i, row in enumerate(lowercase_		):
    for item in row:
     if distinct_edge in item[0]:
      A__ =       "1"
      break
   return "".join(lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	str:
   A__ =       get_distinct_edge(lowercase_		)
   A__ =       {}
  
   for item in distinct_edge:
    A__ =       get_bitcode(lowercase_    ,				lowercase_		)
    # 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 lowercase_		: v[1][0]    ,				reverse=lowercase_		)
   ]
   return sorted_frequency_table
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[int]:
   A__ =       {}
   for _, item in enumerate(lowercase_		):
    nodes.setdefault(item[2]    ,				[]		).append(item[0]		)
   return nodes
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
   A__ =       {}
   for key, value in nodes.items():
    A__ =       value
   return cluster
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
   return [i * 1_00 / len(lowercase_		) for i in cluster]
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	None:
   print("\nNodes\n"		)
   for key, value in nodes.items():
    print(lowercase_    ,				lowercase_		)
   print("\nSupport\n"		)
   print(lowercase_		)
   print("\n Cluster \n"		)
   for key, value in sorted(cluster.items()    ,				reverse=lowercase_		):
    print(lowercase_    ,				lowercase_		)
   print("\n Graph\n"		)
   for key, value in graph.items():
    print(lowercase_    ,				lowercase_		)
   print("\n Edge List of Frequent subgraphs \n"		)
   for edge_list in freq_subgraph_edge_list:
    print(lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	List[Any]:
   for i in cluster[ca]:
    A__ =       0
    A__ =       ca + 1
    while ca < max(cluster.keys()		):
     for j in cluster[ca]:
      if int(lowercase_    ,				2		) & int(lowercase_    ,				2		) == int(lowercase_    ,				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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	List[Any]:
   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(lowercase_		) - 1:
    create_edge(lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)
    A__ =       i + 1
   return graph
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_=None		)		->	List[str]:
   A__ =       (path or []) + [start]
   if start == end:
    paths.append(lowercase_		)
   for node in graph[start]:
    if tuple(lowercase_		) not in path:
     my_dfs(lowercase_    ,				tuple(lowercase_		)    ,				lowercase_    ,				lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Dict:
   A__ =       int(s / 1_00 * (len(lowercase_		) - 1)		)
   for i in cluster[k]:
    my_dfs(lowercase_    ,				tuple(cluster[k][i]		)    ,				(["Header"],)		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
   A__ =       []
   for edges in paths:
    A__ =       []
    for j in range(len(lowercase_		) - 1		):
     A__ =       list(edges[j]		)
     for e in temp:
      A__ =       (e[0], e[1])
      el.append(lowercase_		)
    freq_sub_el.append(lowercase_		)
   return freq_sub_el
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	int:
   for i in range(len(lowercase_		)		):
    for j in range(len(edge_array[i]		)		):
     A__ =       edge_array[i][j].split("-"		)
     A__ =       t
  
  
  if __name__ == "__main__":
        preprocess(edge_array)
        SCREAMING_SNAKE_CASE       = get_frequency_table(edge_array)
        SCREAMING_SNAKE_CASE       = get_nodes(frequency_table)
        SCREAMING_SNAKE_CASE       = get_cluster(nodes)
        SCREAMING_SNAKE_CASE       = get_support(cluster)
        SCREAMING_SNAKE_CASE       = construct_graph(cluster, nodes)
        find_freq_subgraph_given_support(60, cluster, graph)
        SCREAMING_SNAKE_CASE       = []
        SCREAMING_SNAKE_CASE       = freq_subgraphs_edge_list(paths)
        print_all()
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import logging
  from pathlib import Path
  
  import numpy as np
  import pytorch_lightning as pl
  import torch
  from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
  from pytorch_lightning.utilities import rank_zero_only
  
  from utils import save_json
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	List[Any]:
   A__ =       filter(lambda lowercase_		: p.requires_grad    ,				model.parameters()		)
   A__ =       sum([np.prod(p.size()		) for p in model_parameters]		)
   return params
  
  
  SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)
  
  
  
  
  class 			UpperCAmelCase_						(		pl.Callback							):
  
  
  
  
      def 							__magic_name__							(					self		: List[str]			,				snake_case_		: Optional[Any]			,				snake_case_		: Tuple   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
       pl_module.logger.log_metrics(snake_case_   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: Dict			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule			,				snake_case_		: str			,				snake_case_		: List[str]=True   )   ->							None:
  
  
  
  
       '''simple docstring'''
  
       logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
       A__ =       trainer.callback_metrics
       trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}   )
       # Log results
       A__ =       Path(pl_module.hparams.output_dir   )
       if type_path == "test":
        A__ =       od / "test_results.txt"
        A__ =       od / "test_generations.txt"
       else:
        # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
        # If people want this it will be easy enough to add back.
        A__ =       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
        A__ =       od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=snake_case_   )
        generations_file.parent.mkdir(exist_ok=snake_case_   )
       with open(snake_case_			,				"a+"   ) as writer:
        for key in sorted(snake_case_   ):
         if key in ["log", "progress_bar", "preds"]:
          continue
         A__ =       metrics[key]
         if isinstance(snake_case_			,				torch.Tensor   ):
          A__ =       val.item()
         A__ =       F"""{key}: {val:.6f}\n"""
         writer.write(snake_case_   )
  
       if not save_generations:
        return
  
       if "preds" in metrics:
        A__ =       "\n".join(metrics["preds"]   )
        generations_file.open("w+"   ).write(snake_case_   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: str			,				snake_case_		: Tuple			,				snake_case_		: Dict   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       try:
        A__ =       pl_module.model.model.num_parameters()
       except AttributeError:
        A__ =       pl_module.model.num_parameters()
  
       A__ =       count_trainable_parameters(snake_case_   )
       # mp stands for million parameters
       trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: List[Any]			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
       return self._write_logs(snake_case_			,				snake_case_			,				"test"   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: pl.Trainer			,				snake_case_		: int   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
       # Uncommenting this will save val generations
       # return self._write_logs(trainer, pl_module, "valid")
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=1    ,				lowercase_=False		)		->	Dict:
   if metric == "rouge2":
    A__ =       "{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
    A__ =       "{val_avg_bleu:.4f}-{step_count}"
   elif metric == "loss":
    A__ =       "{val_avg_loss:.4f}-{step_count}"
   else:
    raise NotImplementedError(
        f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
        " this function."		)
  
   A__ =       ModelCheckpoint(
       dirpath=lowercase_    ,				filename=lowercase_    ,				monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				save_top_k=lowercase_    ,				)
   return checkpoint_callback
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   return EarlyStopping(
       monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				patience=lowercase_    ,				verbose=lowercase_    ,				)
  
  
  
  
  
  