code: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      Optional[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     UpperCAmelCase__      (			UpperCAmelCase_					:       Dict							)  ->	Any:
       __lowerCamelCase     :			int              =	set()
       for row in edge_array:
            for item in row:
                 distinct_edge.add(item[0]							)
       return list(UpperCAmelCase_							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Optional[Any]							)  ->	Optional[Any]:
       __lowerCamelCase     :			Optional[int]              =	['0'] * len(UpperCAmelCase_							)
       for i, row in enumerate(UpperCAmelCase_							):
            for item in row:
                 if distinct_edge in item[0]:
                      __lowerCamelCase     :			Dict              =	'1'
                      break
       return "".join(UpperCAmelCase_							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       int							)  ->	str:
       __lowerCamelCase     :			Tuple              =	get_distinct_edge(UpperCAmelCase_							)
       __lowerCamelCase     :			Union[str, Any]              =	{}
  
       for item in distinct_edge:
            __lowerCamelCase     :			Union[str, Any]              =	get_bitcode(UpperCAmelCase_   ,					UpperCAmelCase_							)
            # print('bit',bit)
            # bt=''.join(bit)
            __lowerCamelCase     :			List[str]              =	bit.count('1'							)
            __lowerCamelCase     :			Union[str, Any]              =	[s, bit]
       # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
       __lowerCamelCase     :			Optional[Any]              =	[
           [k, v[0], v[1]]
           for k, v in sorted(frequency_table.items()   ,					key=lambda UpperCAmelCase_							: v[1][0]   ,					reverse=UpperCAmelCase_							)
       ]
       return sorted_frequency_table
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Tuple							)  ->	List[Any]:
       __lowerCamelCase     :			Optional[Any]              =	{}
       for _, item in enumerate(UpperCAmelCase_							):
            nodes.setdefault(item[2]   ,					[]							).append(item[0]							)
       return nodes
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Union[str, Any]							)  ->	Union[str, Any]:
       __lowerCamelCase     :			List[Any]              =	{}
       for key, value in nodes.items():
            __lowerCamelCase     :			Tuple              =	value
       return cluster
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	Optional[int]:
       return [i * 1_00 / len(UpperCAmelCase_							) for i in cluster]
  
  
  
  def     UpperCAmelCase__      (			)  ->	None:
       print('\nNodes\n'							)
       for key, value in nodes.items():
            print(UpperCAmelCase_   ,					UpperCAmelCase_							)
       print('\nSupport\n'							)
       print(UpperCAmelCase_							)
       print('\n Cluster \n'							)
       for key, value in sorted(cluster.items()   ,					reverse=UpperCAmelCase_							):
            print(UpperCAmelCase_   ,					UpperCAmelCase_							)
       print('\n Graph\n'							)
       for key, value in graph.items():
            print(UpperCAmelCase_   ,					UpperCAmelCase_							)
       print('\n Edge List of Frequent subgraphs \n'							)
       for edge_list in freq_subgraph_edge_list:
            print(UpperCAmelCase_							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Dict   ,					UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Union[str, Any]							)  ->	List[str]:
       for i in cluster[ca]:
            __lowerCamelCase     :			List[str]              =	0
            __lowerCamelCase     :			List[Any]              =	ca + 1
            while ca < max(cluster.keys()							):
                 for j in cluster[ca]:
                      if int(UpperCAmelCase_   ,					2							) & int(UpperCAmelCase_   ,					2							) == int(UpperCAmelCase_   ,					2							):
                           if tuple(nodes[i]							) in graph:
                                graph[tuple(nodes[i]							)].append(nodes[j]							)
                           else:
                                __lowerCamelCase     :			Optional[int]              =	[nodes[j]]
                           count += 1
                 if count == 0:
                      __lowerCamelCase     :			Any              =	ca + 1
                 else:
                      break
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Tuple   ,					UpperCAmelCase_					:       List[Any]							)  ->	str:
       __lowerCamelCase     :			Dict              =	cluster[max(cluster.keys()							)]
       __lowerCamelCase     :			Tuple              =	'Header'
       __lowerCamelCase     :			str              =	{}
       for i in x:
            if (["Header"],) in graph:
                 graph[(["Header"],)].append(x[i]							)
            else:
                 __lowerCamelCase     :			Dict              =	[x[i]]
       for i in x:
            __lowerCamelCase     :			Optional[Any]              =	[['Header']]
       __lowerCamelCase     :			int              =	1
       while i < max(UpperCAmelCase_							) - 1:
            create_edge(UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_							)
            __lowerCamelCase     :			List[str]              =	i + 1
       return graph
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       List[Any]=None							)  ->	Any:
       __lowerCamelCase     :			Tuple              =	(path or []) + [start]
       if start == end:
            paths.append(UpperCAmelCase_							)
       for node in graph[start]:
            if tuple(UpperCAmelCase_							) not in path:
                 my_dfs(UpperCAmelCase_   ,					tuple(UpperCAmelCase_							)   ,					UpperCAmelCase_   ,					UpperCAmelCase_							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       Tuple							)  ->	List[str]:
       __lowerCamelCase     :			Dict              =	int(s / 1_00 * (len(UpperCAmelCase_							) - 1)							)
       for i in cluster[k]:
            my_dfs(UpperCAmelCase_   ,					tuple(cluster[k][i]							)   ,					(['Header'],)							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	List[Any]:
       __lowerCamelCase     :			Optional[int]              =	[]
       for edges in paths:
            __lowerCamelCase     :			Union[str, Any]              =	[]
            for j in range(len(UpperCAmelCase_							) - 1							):
                 __lowerCamelCase     :			List[str]              =	list(edges[j]							)
                 for e in temp:
                      __lowerCamelCase     :			int              =	(e[0], e[1])
                      el.append(UpperCAmelCase_							)
            freq_sub_el.append(UpperCAmelCase_							)
       return freq_sub_el
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       int							)  ->	Any:
       for i in range(len(UpperCAmelCase_							)							):
            for j in range(len(edge_array[i]							)							):
                 __lowerCamelCase     :			int              =	edge_array[i][j].split('-'							)
                 __lowerCamelCase     :			Optional[int]              =	t
  
  
  if __name__ == "__main__":
        preprocess(edge_array)
        A__	:      Union[str, Any]				=						get_frequency_table(edge_array)
        A__	:      Optional[int]				=						get_nodes(frequency_table)
        A__	:      Any				=						get_cluster(nodes)
        A__	:      Tuple				=						get_support(cluster)
        A__	:      List[Any]				=						construct_graph(cluster, nodes)
        find_freq_subgraph_given_support(60, cluster, graph)
        A__	:      list				=						[]
        A__	:      Tuple				=						freq_subgraphs_edge_list(paths)
        print_all()
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
      lowerCamelCase						:      Tuple							  =    1_0
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           __lowerCamelCase     :			Union[str, Any]              =	{
               'num_train_timesteps': 11_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
           }
  
           config.update(**SCREAMING_SNAKE_CASE_			)
           return config
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
                self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
           __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Any              =	output.prev_sample
  
           __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
           __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Tuple              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
           assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			str              =	self.get_scheduler_config()
           __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
           __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
  
           for t in scheduler.timesteps:
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3
  
  
  
  
  
  
  