code: 
  
  
  '''simple docstring'''
  
  import unittest
  
  from transformers import ErnieMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  							import torch
  
  							from transformers import (
  							    ErnieMForInformationExtraction,
  							    ErnieMForMultipleChoice,
  							    ErnieMForQuestionAnswering,
  							    ErnieMForSequenceClassification,
  							    ErnieMForTokenClassification,
  							    ErnieMModel,
  							)
  							from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		Optional[Any]						,  _A	:		List[Any]						,  _A	:		Dict=13						,  _A	:		int=7						,  _A	:		Optional[int]=True						,  _A	:		str=True						,  _A	:		Optional[int]=True						,  _A	:		Tuple=99						,  _A	:		Tuple=32						,  _A	:		Any=5						,  _A	:		Optional[Any]=4						,  _A	:		int=37						,  _A	:		List[str]="gelu"						,  _A	:		List[Any]=0.1						,  _A	:		Dict=0.1						,  _A	:		Any=512						,  _A	:		Union[str, Any]=16						,  _A	:		Union[str, Any]=2						,  _A	:		str=0.02						,  _A	:		int=3						,  _A	:		List[Any]=4						,  _A	:		Tuple=None						,  )						->				List[str]:
  			__magic_name__ :					Union[str, Any]  			=  parent
  			__magic_name__ :					Optional[int]  			=  batch_size
  			__magic_name__ :					Union[str, Any]  			=  seq_length
  			__magic_name__ :					Union[str, Any]  			=  is_training
  			__magic_name__ :					Optional[Any]  			=  use_input_mask
  			__magic_name__ :					Union[str, Any]  			=  use_labels
  			__magic_name__ :					int  			=  vocab_size
  			__magic_name__ :					int  			=  hidden_size
  			__magic_name__ :					Dict  			=  num_hidden_layers
  			__magic_name__ :					Union[str, Any]  			=  num_attention_heads
  			__magic_name__ :					int  			=  intermediate_size
  			__magic_name__ :					Union[str, Any]  			=  hidden_act
  			__magic_name__ :					Dict  			=  hidden_dropout_prob
  			__magic_name__ :					Optional[int]  			=  attention_probs_dropout_prob
  			__magic_name__ :					Tuple  			=  max_position_embeddings
  			__magic_name__ :					Dict  			=  type_vocab_size
  			__magic_name__ :					Dict  			=  type_sequence_label_size
  			__magic_name__ :					Union[str, Any]  			=  initializer_range
  			__magic_name__ :					Any  			=  num_labels
  			__magic_name__ :					List[Any]  			=  num_choices
  			__magic_name__ :					str  			=  scope
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				Tuple:
  			__magic_name__ :					List[str]  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size    )
  
  			__magic_name__ :					str  			=  None
  			if self.use_input_mask:
  				__magic_name__ :					Tuple  			=  random_attention_mask([self.batch_size, self.seq_length]    )
  
  			__magic_name__ :					Optional[int]  			=  None
  			__magic_name__ :					Dict  			=  None
  			__magic_name__ :					Optional[Any]  			=  None
  			if self.use_labels:
  				__magic_name__ :					Any  			=  ids_tensor([self.batch_size]						,  self.type_sequence_label_size    )
  				__magic_name__ :					Any  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.num_labels    )
  				__magic_name__ :					Tuple  			=  ids_tensor([self.batch_size]						,  self.num_choices    )
  
  			__magic_name__ :					Dict  			=  self.get_config()
  
  			return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Union[str, Any]:
  			__magic_name__ :					List[Any]  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size    )
  
  			__magic_name__ :					Optional[int]  			=  None
  			if self.use_input_mask:
  				__magic_name__ :					Tuple  			=  random_attention_mask([self.batch_size, self.seq_length]    )
  			__magic_name__ :					Optional[Any]  			=  self.get_config()
  
  			return config, input_ids, input_mask
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Any:
  			return ErnieMConfig(
  			    vocab_size=self.vocab_size						,  hidden_size=self.hidden_size						,  num_hidden_layers=self.num_hidden_layers						,  num_attention_heads=self.num_attention_heads						,  intermediate_size=self.intermediate_size						,  hidden_act=self.hidden_act						,  hidden_dropout_prob=self.hidden_dropout_prob						,  attention_probs_dropout_prob=self.attention_probs_dropout_prob						,  max_position_embeddings=self.max_position_embeddings						,  type_vocab_size=self.type_vocab_size						,  initializer_range=self.initializer_range						,  )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		Union[str, Any]						,  _A	:		Optional[int]						,  _A	:		Any						,  _A	:		List[Any]						,  _A	:		int						,  _A	:		Any    )						->				Union[str, Any]:
  			__magic_name__ :					str  			=  ErnieMModel(config=_A    )
  			model.to(_A    )
  			model.eval()
  			__magic_name__ :					List[str]  			=  model(_A						,  return_dict=_A    )
  			self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		List[str]						,  _A	:		List[Any]						,  _A	:		Optional[int]						,  _A	:		Tuple						,  _A	:		Dict						,  _A	:		List[Any]    )						->				Tuple:
  			__magic_name__ :					Any  			=  ErnieMForQuestionAnswering(config=_A    )
  			model.to(_A    )
  			model.eval()
  			__magic_name__ :					List[Any]  			=  model(
  			    _A						,  attention_mask=_A						,  start_positions=_A						,  end_positions=_A						,  )
  			self.parent.assertEqual(result.start_logits.shape						,  (self.batch_size, self.seq_length)    )
  			self.parent.assertEqual(result.end_logits.shape						,  (self.batch_size, self.seq_length)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		Union[str, Any]						,  _A	:		str						,  _A	:		List[str]						,  _A	:		Any						,  _A	:		Any						,  _A	:		str    )						->				Optional[Any]:
  			__magic_name__ :					List[Any]  			=  ErnieMForInformationExtraction(config=_A    )
  			model.to(_A    )
  			model.eval()
  			__magic_name__ :					Optional[int]  			=  torch.ones_like(_A						,  dtype=torch.floataa    )
  			__magic_name__ :					Dict  			=  model(
  			    _A						,  attention_mask=_A						,  start_positions=_A						,  end_positions=_A						,  )
  			self.parent.assertEqual(result.start_logits.shape						,  (self.batch_size, self.seq_length)    )
  			self.parent.assertEqual(result.end_logits.shape						,  (self.batch_size, self.seq_length)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		Union[str, Any]						,  _A	:		str						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]						,  _A	:		List[str]						,  _A	:		List[Any]    )						->				int:
  			__magic_name__ :					List[str]  			=  self.num_labels
  			__magic_name__ :					int  			=  ErnieMForSequenceClassification(_A    )
  			model.to(_A    )
  			model.eval()
  			__magic_name__ :					str  			=  model(_A						,  attention_mask=_A						,  labels=_A    )
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		List[str]						,  _A	:		Any						,  _A	:		Tuple						,  _A	:		Dict						,  _A	:		str						,  _A	:		List[str]    )						->				Dict:
  			__magic_name__ :					Optional[Any]  			=  self.num_labels
  			__magic_name__ :					Optional[int]  			=  ErnieMForTokenClassification(config=_A    )
  			model.to(_A    )
  			model.eval()
  			input_ids.to(_A    )
  			input_mask.to(_A    )
  			token_labels.to(_A    )
  
  			__magic_name__ :					List[Any]  			=  model(_A						,  attention_mask=_A						,  labels=_A    )
  
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		Optional[int]						,  _A	:		Optional[Any]						,  _A	:		str						,  _A	:		int						,  _A	:		List[str]						,  _A	:		Dict    )						->				List[str]:
  			__magic_name__ :					List[str]  			=  self.num_choices
  			__magic_name__ :					Any  			=  ErnieMForMultipleChoice(config=_A    )
  			model.to(_A    )
  			model.eval()
  			__magic_name__ :					List[Any]  			=  input_ids.unsqueeze(1    ).expand(-1						,  self.num_choices						,  -1    ).contiguous()
  			__magic_name__ :					Tuple  			=  input_mask.unsqueeze(1    ).expand(-1						,  self.num_choices						,  -1    ).contiguous()
  			__magic_name__ :					Union[str, Any]  			=  model(
  			    _A						,  attention_mask=_A						,  labels=_A						,  )
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_choices)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				Any:
  			__magic_name__ :					Optional[int]  			=  self.prepare_config_and_inputs()
  			(
  			    __magic_name__       
  			) :					int  			=  config_and_inputs
  			__magic_name__ :					str  			=  {'input_ids': input_ids, 'attention_mask': input_mask}
  			return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class _lowerCamelCase (		lowercase__	,  lowercase__	,  unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[int]   					=       (
  		    (
  		        ErnieMModel,
  		        ErnieMForMultipleChoice,
  		        ErnieMForQuestionAnswering,
  		        ErnieMForSequenceClassification,
  		        ErnieMForTokenClassification,
  		    )
  		    if is_torch_available()
  		    else ()
  		)
  		A_			:    str   					=       ()
  		A_			:    Optional[int]   					=       (
  		    {
  		        """feature-extraction""": ErnieMModel,
  		        """question-answering""": ErnieMForQuestionAnswering,
  		        """text-classification""": ErnieMForSequenceClassification,
  		        """token-classification""": ErnieMForTokenClassification,
  		        """zero-shot""": ErnieMForSequenceClassification,
  		    }
  		    if is_torch_available()
  		    else {}
  		)
  		A_			:    Tuple   					=       False
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]						,  _A	:		Optional[int]						,  _A	:		Any						,  _A	:		Optional[int]						,  _A	:		str						,  _A	:		List[str]    )						->				Any:
  			if pipeline_test_casse_name == "QAPipelineTests":
  				return True
  
  			return False
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				Tuple:
  			__magic_name__ :					Any  			=  ErnieMModelTester(self    )
  			__magic_name__ :					Optional[int]  			=  ConfigTester(self						,  config_class=_A						,  hidden_size=37    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				int:
  			self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int    )						->				Optional[int]:
  			__magic_name__ :					Union[str, Any]  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_model(*_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				Dict:
  			__magic_name__ :					Tuple  			=  self.model_tester.prepare_config_and_inputs()
  			for type in ["absolute", "relative_key", "relative_key_query"]:
  				__magic_name__ :					int  			=  type
  				self.model_tester.create_and_check_model(*_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int    )						->				int:
  			__magic_name__ :					Optional[int]  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_for_multiple_choice(*_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				Any:
  			__magic_name__ :					str  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_for_question_answering(*_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				str:
  			__magic_name__ :					List[str]  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_for_sequence_classification(*_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Dict:
  			__magic_name__ :					int  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_for_information_extraction(*_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Dict:
  			__magic_name__ :					Dict  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_for_token_classification(*_A    )
  
  
  
  
  
  
  
  		@slow
  		def      __lowerCAmelCase	(      self	:		int    )						->				Any:
  			for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  				__magic_name__ :					Dict  			=  ErnieMModel.from_pretrained(_A    )
  				self.assertIsNotNone(_A    )
  
  
  
  
  
  
  
  @require_torch
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		@slow
  		def      __lowerCAmelCase	(      self	:		int    )						->				Optional[Any]:
  			__magic_name__ :					Any  			=  ErnieMModel.from_pretrained('susnato/ernie-m-base_pytorch'    )
  			model.eval()
  			__magic_name__ :					Optional[int]  			=  torch.tensor([[0, 1, 2, 3, 4, 5]]    )
  			__magic_name__ :					List[str]  			=  model(_A    )[0]
  
  			# TODO Replace vocab size
  			__magic_name__ :					int  			=  768
  
  			__magic_name__ :					Optional[int]  			=  torch.Size((1, 6, hidden_size)    )
  			self.assertEqual(output.shape						,  _A    )
  
  			__magic_name__ :					Tuple  			=  torch.tensor(
  			    [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]    )
  
  			self.assertTrue(torch.allclose(output[:, :3, :3]						,  _A						,  atol=1E-3    )    )
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()