code: 
  
  
  '''simple docstring'''
  
  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
  lowerCAmelCase   :Union[str, Any]								=    False
  lowerCAmelCase   :int								=    Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
  lowerCAmelCase   :Optional[Any]								=    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, 2_0_4_8), dtype='''float32'''),
              '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
              '''preds_per_image''': datasets.Value(dtype='''int32'''),
          }
      )
  )
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		int						,  _A	:		Any=sys.argv[1:]    )						->				Any:
  			__magic_name__ :					int  			=  None
  			__magic_name__ :					Any  			=  None
  			__magic_name__ :					Any  			=  None
  			__magic_name__ :					Union[str, Any]  			=  1
  			__magic_name__ :					Optional[int]  			=  getopt.getopt(_snake_case						,  'i:o:b:s'						,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']    )
  			for opt, arg in opts:
  				if opt in ("-i", "--inputdir"):
  					__magic_name__ :					Union[str, Any]  			=  arg
  				elif opt in ("-o", "--outfile"):
  					__magic_name__ :					Union[str, Any]  			=  arg
  				elif opt in ("-b", "--batch_size"):
  					__magic_name__ :					str  			=  int(_snake_case    )
  				elif opt in ("-s", "--subset_list"):
  					__magic_name__ :					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:
  					__magic_name__ :					Union[str, Any]  			=  {self._vqa_file_split()[0] for x in tryload(_snake_case    )}
  			else:
  				__magic_name__ :					Optional[Any]  			=  None
  
  			__magic_name__ :					Tuple  			=  CONFIG
  			if torch.cuda.is_available():
  				__magic_name__ :					Tuple  			=  '''cuda'''
  			__magic_name__ :					List[Any]  			=  os.path.realpath(_snake_case    )
  			__magic_name__ :					int  			=  os.path.realpath(_snake_case    )
  			__magic_name__ :					List[str]  			=  Preprocess(self.config    )
  			__magic_name__ :					int  			=  GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'						,  config=self.config    )
  			__magic_name__ :					Optional[int]  			=  batch_size if batch_size != 0 else 1
  			__magic_name__ :					Dict  			=  DEFAULT_SCHEMA
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Dict    )						->				Optional[Any]:
  			__magic_name__ :					Tuple  			=  int(file.split('.'    )[0].split('_'    )[-1]    )
  			__magic_name__ :					str  			=  os.path.join(self.inputdir						,  _snake_case    )
  			return (img_id, filepath)
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				List[str]:
  			__magic_name__ :					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:
  					__magic_name__ :					int  			=  batch
  					__magic_name__ :					Dict  			=  []
  					yield list(map(_snake_case						,  zip(*_snake_case    )    )    )
  
  			for i in range(1    ):
  				yield list(map(_snake_case						,  zip(*_snake_case    )    )    )
  
  
  
  
  
  
  
  		def __call__(      self	:		Optional[Any]    )						->				str:
  			# make writer
  			if not TEST:
  				__magic_name__ :					List[str]  			=  datasets.ArrowWriter(features=self.schema						,  path=self.outputfile    )
  			# do file generator
  			for i, (img_ids, filepaths) in enumerate(self.file_generator    ):
  				__magic_name__ :					Union[str, Any]  			=  self.preprocess(_snake_case    )
  				__magic_name__ :					List[str]  			=  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'						,  )
  				__magic_name__ :					Optional[int]  			=  output_dict.pop('normalized_boxes'    )
  				if not TEST:
  					__magic_name__ :					List[Any]  			=  np.array(_snake_case    )
  					__magic_name__ :					str  			=  self.schema.encode_batch(_snake_case    )
  					writer.write_batch(_snake_case    )
  				if TEST:
  					break
  				# finalizer the writer
  			if not TEST:
  				__magic_name__ :					str  			=  writer.finalize()
  				print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'    )
  
  
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	try:
  		__magic_name__ :					int  			=  json.load(lowerCAmelCase	)
  		try:
  			__magic_name__ :					str  			=  list(data.keys()	)
  		except Exception:
  			__magic_name__ :					str  			=  [d['''img_id'''] for d in data]
  	except Exception:
  		try:
  			__magic_name__ :					Tuple  			=  eval(stream.read()	)
  		except Exception:
  			__magic_name__ :					List[Any]  			=  stream.read().split('\n'	)
  	return data
  
  
  if __name__ == "__main__":
  							lowerCAmelCase   :Any								=    Extract(sys.argv[1:])
  							extract()
  							if not TEST:
  														lowerCAmelCase   :Dict								=    datasets.Dataset.from_file(extract.outputfile)
  														# wala!
  														# print(np.array(dataset[0:2]["roi_features"]).shape)
style_context: 
  
  
  '''simple docstring'''
  
  lowerCAmelCase   :int								=    [
      ['''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    (    lowerCAmelCase       :			Tuple	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Union[str, Any]  			=  set()
  	for row in edge_array:
  		for item in row:
  			distinct_edge.add(item[0]	)
  	return list(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Optional[int]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[Any]  			=  ['0'] * len(lowerCAmelCase	)
  	for i, row in enumerate(lowerCAmelCase	):
  		for item in row:
  			if distinct_edge in item[0]:
  				__magic_name__ :					str  			=  '1'
  				break
  	return "".join(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Tuple	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Any  			=  get_distinct_edge(lowerCAmelCase	)
  	__magic_name__ :					Union[str, Any]  			=  {}
  
  	for item in distinct_edge:
  		__magic_name__ :					Union[str, Any]  			=  get_bitcode(lowerCAmelCase	,	lowerCAmelCase	)
  		# print('bit',bit)
  		# bt=''.join(bit)
  		__magic_name__ :					Dict  			=  bit.count('1'	)
  		__magic_name__ :					Dict  			=  [s, bit]
  	# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
  	__magic_name__ :					Optional[Any]  			=  [
  	    [k, v[0], v[1]]
  	    for k, v in sorted(frequency_table.items()	,	key=lambda lowerCAmelCase	: v[1][0]	,	reverse=lowerCAmelCase	)
  	]
  	return sorted_frequency_table
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Tuple  			=  {}
  	for _, item in enumerate(lowerCAmelCase	):
  		nodes.setdefault(item[2]	,	[]	).append(item[0]	)
  	return nodes
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  {}
  	for key, value in nodes.items():
  		__magic_name__ :					Union[str, Any]  			=  value
  	return cluster
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	return [i * 100 / len(lowerCAmelCase	) for i in cluster]
  
  
  
  
  
  def lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	print('\nNodes\n'	)
  	for key, value in nodes.items():
  		print(lowerCAmelCase	,	lowerCAmelCase	)
  	print('\nSupport\n'	)
  	print(lowerCAmelCase	)
  	print('\n Cluster \n'	)
  	for key, value in sorted(cluster.items()	,	reverse=lowerCAmelCase	):
  		print(lowerCAmelCase	,	lowerCAmelCase	)
  	print('\n Graph\n'	)
  	for key, value in graph.items():
  		print(lowerCAmelCase	,	lowerCAmelCase	)
  	print('\n Edge List of Frequent subgraphs \n'	)
  	for edge_list in freq_subgraph_edge_list:
  		print(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for i in cluster[ca]:
  		__magic_name__ :					Tuple  			=  0
  		__magic_name__ :					Any  			=  ca + 1
  		while ca < max(cluster.keys()	):
  			for j in cluster[ca]:
  				if int(lowerCAmelCase	,	2	) & int(lowerCAmelCase	,	2	) == int(lowerCAmelCase	,	2	):
  					if tuple(nodes[i]	) in graph:
  						graph[tuple(nodes[i]	)].append(nodes[j]	)
  					else:
  						__magic_name__ :					List[Any]  			=  [nodes[j]]
  					count += 1
  			if count == 0:
  				__magic_name__ :					Tuple  			=  ca + 1
  			else:
  				break
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Any	,	lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  cluster[max(cluster.keys()	)]
  	__magic_name__ :					Dict  			=  'Header'
  	__magic_name__ :					Optional[Any]  			=  {}
  	for i in x:
  		if (["Header"],) in graph:
  			graph[(["Header"],)].append(x[i]	)
  		else:
  			__magic_name__ :					List[str]  			=  [x[i]]
  	for i in x:
  		__magic_name__ :					Union[str, Any]  			=  [['Header']]
  	__magic_name__ :					List[str]  			=  1
  	while i < max(lowerCAmelCase	) - 1:
  		create_edge(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  		__magic_name__ :					Dict  			=  i + 1
  	return graph
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			Union[str, Any]=None	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  (path or []) + [start]
  	if start == end:
  		paths.append(lowerCAmelCase	)
  	for node in graph[start]:
  		if tuple(lowerCAmelCase	) not in path:
  			my_dfs(lowerCAmelCase	,	tuple(lowerCAmelCase	)	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Union[str, Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Union[str, Any]  			=  int(s / 100 * (len(lowerCAmelCase	) - 1)	)
  	for i in cluster[k]:
  		my_dfs(lowerCAmelCase	,	tuple(cluster[k][i]	)	,	(['Header'],)	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  []
  	for edges in paths:
  		__magic_name__ :					List[Any]  			=  []
  		for j in range(len(lowerCAmelCase	) - 1	):
  			__magic_name__ :					List[str]  			=  list(edges[j]	)
  			for e in temp:
  				__magic_name__ :					Optional[int]  			=  (e[0], e[1])
  				el.append(lowerCAmelCase	)
  		freq_sub_el.append(lowerCAmelCase	)
  	return freq_sub_el
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for i in range(len(lowerCAmelCase	)	):
  		for j in range(len(edge_array[i]	)	):
  			__magic_name__ :					Any  			=  edge_array[i][j].split('-'	)
  			__magic_name__ :					Tuple  			=  t
  
  
  if __name__ == "__main__":
  							preprocess(edge_array)
  							lowerCAmelCase   :List[str]								=    get_frequency_table(edge_array)
  							lowerCAmelCase   :List[str]								=    get_nodes(frequency_table)
  							lowerCAmelCase   :Optional[int]								=    get_cluster(nodes)
  							lowerCAmelCase   :Tuple								=    get_support(cluster)
  							lowerCAmelCase   :Tuple								=    construct_graph(cluster, nodes)
  							find_freq_subgraph_given_support(6_0, cluster, graph)
  							lowerCAmelCase   :list								=    []
  							lowerCAmelCase   :str								=    freq_subgraphs_edge_list(paths)
  							print_all()