


snake_case			:      List[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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()
				for row in edge_array:
								for item in row:
												distinct_edge.add(item[0]							)
				return list(UpperCAmelCase__							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = ['0'] * len(UpperCAmelCase__							)
				for i, row in enumerate(UpperCAmelCase__							):
								for item in row:
												if distinct_edge in item[0]:
																_SCREAMING_SNAKE_CASE      = '1'
																break
				return "".join(UpperCAmelCase__							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = get_distinct_edge(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = {}

				for item in distinct_edge:
								_SCREAMING_SNAKE_CASE      = get_bitcode(UpperCAmelCase__   ,UpperCAmelCase__							)
								# print('bit',bit)
								# bt=''.join(bit)
								_SCREAMING_SNAKE_CASE      = bit.count('1'							)
								_SCREAMING_SNAKE_CASE      = [s, bit]
				# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
				_SCREAMING_SNAKE_CASE      = [
				    [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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = {}
				for _, item in enumerate(UpperCAmelCase__							):
								nodes.setdefault(item[2]   ,[]							).append(item[0]							)
				return nodes


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = {}
				for key, value in nodes.items():
								_SCREAMING_SNAKE_CASE      = value
				return cluster


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				return [i * 100 / len(UpperCAmelCase__							) for i in cluster]


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				for i in cluster[ca]:
								_SCREAMING_SNAKE_CASE      = 0
								_SCREAMING_SNAKE_CASE      = 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:
																								_SCREAMING_SNAKE_CASE      = [nodes[j]]
																				count += 1
												if count == 0:
																_SCREAMING_SNAKE_CASE      = ca + 1
												else:
																break


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = cluster[max(cluster.keys()							)]
				_SCREAMING_SNAKE_CASE      = 'Header'
				_SCREAMING_SNAKE_CASE      = {}
				for i in x:
								if (["Header"],) in graph:
												graph[(["Header"],)].append(x[i]							)
								else:
												_SCREAMING_SNAKE_CASE      = [x[i]]
				for i in x:
								_SCREAMING_SNAKE_CASE      = [['Header']]
				_SCREAMING_SNAKE_CASE      = 1
				while i < max(UpperCAmelCase__							) - 1:
								create_edge(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							)
								_SCREAMING_SNAKE_CASE      = i + 1
				return graph


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=None							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = (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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = int(s / 100 * (len(UpperCAmelCase__							) - 1)							)
				for i in cluster[k]:
								my_dfs(UpperCAmelCase__   ,tuple(cluster[k][i]							)   ,(['Header'],)							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = []
				for edges in paths:
								_SCREAMING_SNAKE_CASE      = []
								for j in range(len(UpperCAmelCase__							) - 1							):
												_SCREAMING_SNAKE_CASE      = list(edges[j]							)
												for e in temp:
																_SCREAMING_SNAKE_CASE      = (e[0], e[1])
																el.append(UpperCAmelCase__							)
								freq_sub_el.append(UpperCAmelCase__							)
				return freq_sub_el


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				for i in range(len(UpperCAmelCase__							)							):
								for j in range(len(edge_array[i]							)							):
												_SCREAMING_SNAKE_CASE      = edge_array[i][j].split('-'							)
												_SCREAMING_SNAKE_CASE      = t


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




import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				if isinstance(UpperCAmelCase__   ,(list, tuple)							):
								return honor_type(UpperCAmelCase__   ,(move_to_device(UpperCAmelCase__   ,UpperCAmelCase__							) for t in state)							)
				elif isinstance(UpperCAmelCase__   ,UpperCAmelCase__							):
								return type(UpperCAmelCase__							)({k: move_to_device(UpperCAmelCase__   ,UpperCAmelCase__							) for k, v in state.items()}							)
				elif isinstance(UpperCAmelCase__   ,torch.Tensor							):
								return state.to(UpperCAmelCase__							)
				return state



class    __lowercase  (       torch.optim.Optimizer							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_=True				, A_=None	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = optimizer
											_SCREAMING_SNAKE_CASE      = scaler
											_SCREAMING_SNAKE_CASE      = AcceleratorState()
											_SCREAMING_SNAKE_CASE      = GradientState()
											_SCREAMING_SNAKE_CASE      = device_placement
											_SCREAMING_SNAKE_CASE      = False
											_SCREAMING_SNAKE_CASE      = None

											# Handle device placement
											if device_placement:
															_SCREAMING_SNAKE_CASE      = self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(A_				, self.accelerator_state.device	)
															else:
																			_SCREAMING_SNAKE_CASE      = move_to_device(A_				, self.accelerator_state.device	)
															self.optimizer.load_state_dict(A_	)





							@property
							def 		__magic_name__			(    self	)->	Tuple:
											return self.optimizer.state





							@state.setter
							def 		__magic_name__			(    self				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = state





							@property
							def 		__magic_name__			(    self	)->	Optional[Any]:
											return self.optimizer.param_groups





							@param_groups.setter
							def 		__magic_name__			(    self				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = param_groups





							@property
							def 		__magic_name__			(    self	)->	Any:
											return self.optimizer.defaults





							@defaults.setter
							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = defaults





							def 		__magic_name__			(    self				, A_	)->	Any:
											self.optimizer.add_param_group(A_	)





							def 		__magic_name__			(    self				, A_	)->	int:
											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(A_				, self.accelerator_state.device	)
											self.optimizer.load_state_dict(A_	)





							def 		__magic_name__			(    self	)->	List[str]:
											return self.optimizer.state_dict()





							def 		__magic_name__			(    self				, A_=None	)->	Any:
											if self.gradient_state.sync_gradients:
															_SCREAMING_SNAKE_CASE      = 'set_to_none' in inspect.signature(self.optimizer.zero_grad	).parameters
															if accept_arg:
																			if set_to_none is None:
																							_SCREAMING_SNAKE_CASE      = False
																			self.optimizer.zero_grad(set_to_none=A_	)
															else:
																			if set_to_none is not None:
																							raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'	)
																			self.optimizer.zero_grad()





							def 		__magic_name__			(    self				, A_=None	)->	Optional[int]:
											if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			_SCREAMING_SNAKE_CASE      = {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer				, optimizer_args=A_	)
															elif self.scaler is not None:
																			_SCREAMING_SNAKE_CASE      = False
																			if self._last_scale is None:
																							# `get_scale` is an async operation requiring full synchronization
																							# on CPU and GPUs before finishing. As a result, we store away
																							# the prior one to reduce the call overhead
																							_SCREAMING_SNAKE_CASE      = self.scaler.get_scale()
																							_SCREAMING_SNAKE_CASE      = True
																			self.scaler.step(self.optimizer				, A_	)
																			self.scaler.update()
																			_SCREAMING_SNAKE_CASE      = self.scaler.get_scale()
																			if not new_scale:
																							# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																							_SCREAMING_SNAKE_CASE      = scale_after < self._last_scale
																			_SCREAMING_SNAKE_CASE      = scale_after

															else:
																			self.optimizer.step(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											for param_group in self.optimizer.param_groups:
															_SCREAMING_SNAKE_CASE      = [parameters_map.get(A_				, A_	) for p in param_group['params']]





							@property
							def 		__magic_name__			(    self	)->	str:
											warnings.warn(
											    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
											    '`optimizer.step_was_skipped` instead.'				, A_				, )
											return self._is_overflow





							@property
							def 		__magic_name__			(    self	)->	int:
											return self._is_overflow





							def __getstate__(    self	)->	Dict:
											return self.__dict__.copy()





							def __setstate__(    self				, A_	)->	List[str]:
											self.__dict__.update(A_	)
