




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()











"""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_    ,				)





