
lowercase						:      Union[str, Any]							=							[
    ["""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 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       lowercase							:  List[str] 			=   set()
       for row in edge_array:
              for item in row:
                     distinct_edge.add(item[0]       )
       return list(SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  List[Any] 			=   ["""0"""] * len(SCREAMING_SNAKE_CASE__       )
       for i, row in enumerate(SCREAMING_SNAKE_CASE__       ):
              for item in row:
                     if distinct_edge in item[0]:
                            lowercase							:  List[Any] 			=   """1"""
                            break
       return "".join(SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  Optional[Any] 			=   get_distinct_edge(SCREAMING_SNAKE_CASE__       )
       lowercase							:  Union[str, Any] 			=   {}

       for item in distinct_edge:
              lowercase							:  Tuple 			=   get_bitcode(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
              # print('bit',bit)
              # bt=''.join(bit)
              lowercase							:  Union[str, Any] 			=   bit.count("""1"""       )
              lowercase							:  Optional[int] 			=   [s, bit]
       # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
       lowercase							:  Tuple 			=   [
           [k, v[0], v[1]]
           for k, v in sorted(frequency_table.items()      ,			key=lambda SCREAMING_SNAKE_CASE__       : v[1][0]      ,			reverse=SCREAMING_SNAKE_CASE__       )
       ]
       return sorted_frequency_table







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					str:
       lowercase							:  Dict 			=   {}
       for _, item in enumerate(SCREAMING_SNAKE_CASE__       ):
              nodes.setdefault(item[2]      ,			[]       ).append(item[0]       )
       return nodes







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Tuple:
       lowercase							:  int 			=   {}
       for key, value in nodes.items():
              lowercase							:  str 			=   value
       return cluster







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       return [i * 100 / len(SCREAMING_SNAKE_CASE__       ) for i in cluster]







def 		_snake_case(						)				->					None:
       print("""\nNodes\n"""       )
       for key, value in nodes.items():
              print(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       print("""\nSupport\n"""       )
       print(SCREAMING_SNAKE_CASE__       )
       print("""\n Cluster \n"""       )
       for key, value in sorted(cluster.items()      ,			reverse=SCREAMING_SNAKE_CASE__       ):
              print(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       print("""\n Graph\n"""       )
       for key, value in graph.items():
              print(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       print("""\n Edge List of Frequent subgraphs \n"""       )
       for edge_list in freq_subgraph_edge_list:
              print(SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Any:
       for i in cluster[ca]:
              lowercase							:  Tuple 			=   0
              lowercase							:  Dict 			=   ca + 1
              while ca < max(cluster.keys()       ):
                     for j in cluster[ca]:
                            if int(SCREAMING_SNAKE_CASE__      ,			2       ) & int(SCREAMING_SNAKE_CASE__      ,			2       ) == int(SCREAMING_SNAKE_CASE__      ,			2       ):
                                   if tuple(nodes[i]       ) in graph:
                                          graph[tuple(nodes[i]       )].append(nodes[j]       )
                                   else:
                                          lowercase							:  str 			=   [nodes[j]]
                                   count += 1
                     if count == 0:
                            lowercase							:  List[str] 			=   ca + 1
                     else:
                            break







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       lowercase							:  Optional[int] 			=   cluster[max(cluster.keys()       )]
       lowercase							:  List[str] 			=   """Header"""
       lowercase							:  Tuple 			=   {}
       for i in x:
              if (["Header"],) in graph:
                     graph[(["Header"],)].append(x[i]       )
              else:
                     lowercase							:  Optional[Any] 			=   [x[i]]
       for i in x:
              lowercase							:  Optional[int] 			=   [["""Header"""]]
       lowercase							:  Optional[int] 			=   1
       while i < max(SCREAMING_SNAKE_CASE__       ) - 1:
              create_edge(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
              lowercase							:  Any 			=   i + 1
       return graph







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=None       )				->					Any:
       lowercase							:  str 			=   (path or []) + [start]
       if start == end:
              paths.append(SCREAMING_SNAKE_CASE__       )
       for node in graph[start]:
              if tuple(SCREAMING_SNAKE_CASE__       ) not in path:
                     my_dfs(SCREAMING_SNAKE_CASE__      ,			tuple(SCREAMING_SNAKE_CASE__       )      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       lowercase							:  int 			=   int(s / 100 * (len(SCREAMING_SNAKE_CASE__       ) - 1)       )
       for i in cluster[k]:
              my_dfs(SCREAMING_SNAKE_CASE__      ,			tuple(cluster[k][i]       )      ,			(["""Header"""],)       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Dict:
       lowercase							:  List[str] 			=   []
       for edges in paths:
              lowercase							:  str 			=   []
              for j in range(len(SCREAMING_SNAKE_CASE__       ) - 1       ):
                     lowercase							:  Tuple 			=   list(edges[j]       )
                     for e in temp:
                            lowercase							:  Optional[Any] 			=   (e[0], e[1])
                            el.append(SCREAMING_SNAKE_CASE__       )
              freq_sub_el.append(SCREAMING_SNAKE_CASE__       )
       return freq_sub_el







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Any:
       for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
              for j in range(len(edge_array[i]       )       ):
                     lowercase							:  Optional[Any] 			=   edge_array[i][j].split("""-"""       )
                     lowercase							:  Optional[int] 			=   t


if __name__ == "__main__":
  preprocess(edge_array)
  lowercase						:      Optional[Any]							=							get_frequency_table(edge_array)
  lowercase						:      Optional[int]							=							get_nodes(frequency_table)
  lowercase						:      List[str]							=							get_cluster(nodes)
  lowercase						:      Tuple							=							get_support(cluster)
  lowercase						:      int							=							construct_graph(cluster, nodes)
  find_freq_subgraph_given_support(60, cluster, graph)
  lowercase						:      list							=							[]
  lowercase						:      Any							=							freq_subgraphs_edge_list(paths)
  print_all()


import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
lowercase						:      List[Any]							=							False
lowercase						:      Union[str, Any]							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase						:      List[str]							=							datasets.Features(
    OrderedDict(
        {
            """attr_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """attr_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """boxes""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="""float32"""),
            """img_id""": datasets.Value("""int32"""),
            """obj_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """obj_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """roi_features""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)

class __snake_case	:
    def __init__(						self	,snake_case=sys.argv[1:]   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   None
           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Tuple 			=   None
           lowercase							:  Optional[int] 			=   1
           lowercase			,    lowercase							:  str 			=   getopt.getopt(snake_case	,"""i:o:b:s"""	,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
           for opt, arg in opts:
                  if opt in ("-i", "--inputdir"):
                         lowercase							:  List[Any] 			=   arg
                  elif opt in ("-o", "--outfile"):
                         lowercase							:  Any 			=   arg
                  elif opt in ("-b", "--batch_size"):
                         lowercase							:  Any 			=   int(snake_case   )
                  elif opt in ("-s", "--subset_list"):
                         lowercase							:  str 			=   arg

           assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
           assert outputfile is not None and not os.path.isfile(snake_case   ), f"{outputfile}"
           if subset_list is not None:
                  with open(os.path.realpath(snake_case   )   ) as f:
                         lowercase							:  Dict 			=   {self._vqa_file_split()[0] for x in tryload(snake_case   )}
           else:
                  lowercase							:  Optional[Any] 			=   None

           lowercase							:  Optional[Any] 			=   CONFIG
           if torch.cuda.is_available():
                  lowercase							:  Union[str, Any] 			=   """cuda"""
           lowercase							:  Dict 			=   os.path.realpath(snake_case   )
           lowercase							:  List[Any] 			=   os.path.realpath(snake_case   )
           lowercase							:  Tuple 			=   Preprocess(self.config   )
           lowercase							:  Any 			=   GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""	,config=self.config   )
           lowercase							:  Tuple 			=   batch_size if batch_size != 0 else 1
           lowercase							:  List[str] 			=   DEFAULT_SCHEMA
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
           lowercase							:  List[str] 			=   os.path.join(self.inputdir	,snake_case   )
           return (img_id, filepath)
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
           for i, file in enumerate(os.listdir(self.inputdir   )   ):
                  if self.subset_list is not None and i not in self.subset_list:
                         continue
                  batch.append(self._vqa_file_split(snake_case   )   )
                  if len(snake_case   ) == self.batch:
                         lowercase							:  Dict 			=   batch
                         lowercase							:  int 			=   []
                         yield list(map(snake_case	,zip(*snake_case   )   )   )

           for i in range(1   ):
                  yield list(map(snake_case	,zip(*snake_case   )   )   )
    def __call__(						self   ):



           '''simple docstring'''







           if not TEST:
                  lowercase							:  Any 			=   datasets.ArrowWriter(features=self.schema	,path=self.outputfile   )
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                  lowercase			,    lowercase			,    lowercase							:  Tuple 			=   self.preprocess(snake_case   )
                  lowercase							:  Dict 			=   self.model(
                      snake_case	,snake_case	,scales_yx=snake_case	,padding="""max_detections"""	,max_detections=self.config.MAX_DETECTIONS	,pad_value=0	,return_tensors="""np"""	,location="""cpu"""	,)
                  lowercase							:  int 			=   output_dict.pop("""normalized_boxes"""   )
                  if not TEST:
                         lowercase							:  Optional[int] 			=   np.array(snake_case   )
                         lowercase							:  Tuple 			=   self.schema.encode_batch(snake_case   )
                         writer.write_batch(snake_case   )
                  if TEST:
                         break
                  # finalizer the writer
           if not TEST:
                  lowercase			,    lowercase							:  Dict 			=   writer.finalize()
                  print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       try:
              lowercase							:  Dict 			=   json.load(SCREAMING_SNAKE_CASE__       )
              try:
                     lowercase							:  Tuple 			=   list(data.keys()       )
              except Exception:
                     lowercase							:  Tuple 			=   [d["""img_id"""] for d in data]
       except Exception:
              try:
                     lowercase							:  Optional[Any] 			=   eval(stream.read()       )
              except Exception:
                     lowercase							:  Any 			=   stream.read().split("""\n"""       )
       return data


if __name__ == "__main__":
  lowercase						:      Optional[int]							=							Extract(sys.argv[1:])
  extract()
  if not TEST:
    lowercase						:      List[Any]							=							datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)
