


'''simple docstring'''

lowerCamelCase							:       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				(A	)	->	List[str]:






							"""simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	List[Any]:






							"""simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A	)	->	int:






							"""simple docstring"""







							lowercase__				      =			get_distinct_edge(snake_case__	)
							lowercase__				      =			{}

							for item in distinct_edge:
														lowercase__				      =			get_bitcode(snake_case__  ,			snake_case__	)
														# print('bit',bit)
														# bt=''.join(bit)
														lowercase__				      =			bit.count('''1'''	)
														lowercase__				      =			[s, bit]
							# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
							lowercase__				      =			[
							    [k, v[0], v[1]]
							    for k, v in sorted(frequency_table.items()  ,			key=lambda A	: v[1][0]  ,			reverse=snake_case__	)
							]
							return sorted_frequency_table

def _SCREAMING_SNAKE_CASE				(A	)	->	Optional[int]:






							"""simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A	)	->	Tuple:






							"""simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A	)	->	Tuple:






							"""simple docstring"""







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

def _SCREAMING_SNAKE_CASE				()	->	None:






							"""simple docstring"""







							print('''\nNodes\n'''	)
							for key, value in nodes.items():
														print(snake_case__  ,			snake_case__	)
							print('''\nSupport\n'''	)
							print(snake_case__	)
							print('''\n Cluster \n'''	)
							for key, value in sorted(cluster.items()  ,			reverse=snake_case__	):
														print(snake_case__  ,			snake_case__	)
							print('''\n Graph\n'''	)
							for key, value in graph.items():
														print(snake_case__  ,			snake_case__	)
							print('''\n Edge List of Frequent subgraphs \n'''	)
							for edge_list in freq_subgraph_edge_list:
														print(snake_case__	)

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A	)	->	str:






							"""simple docstring"""







							for i in cluster[ca]:
														lowercase__				      =			0
														lowercase__				      =			ca + 1
														while ca < max(cluster.keys()	):
																					for j in cluster[ca]:
																												if int(snake_case__  ,			2	) & int(snake_case__  ,			2	) == int(snake_case__  ,			2	):
																																			if tuple(nodes[i]	) in graph:
																																										graph[tuple(nodes[i]	)].append(nodes[j]	)
																																			else:
																																										lowercase__				      =			[nodes[j]]
																																			count += 1
																					if count == 0:
																												lowercase__				      =			ca + 1
																					else:
																												break

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	Tuple:






							"""simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A=None	)	->	Union[str, Any]:






							"""simple docstring"""







							lowercase__				      =			(path or []) + [start]
							if start == end:
														paths.append(snake_case__	)
							for node in graph[start]:
														if tuple(snake_case__	) not in path:
																					my_dfs(snake_case__  ,			tuple(snake_case__	)  ,			snake_case__  ,			snake_case__	)

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A	)	->	str:






							"""simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A	)	->	int:






							"""simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A	)	->	List[str]:






							"""simple docstring"""







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


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








'''simple docstring'''

import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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

def _SCREAMING_SNAKE_CASE				(A  ,			A = False	)	->	Tuple:






       """simple docstring"""







       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(A	)
       np.random.seed(A	)
       torch.manual_seed(A	)
       if is_xpu_available():
              torch.xpu.manual_seed_all(A	)
       elif is_npu_available():
              torch.npu.manual_seed_all(A	)
       else:
              torch.cuda.manual_seed_all(A	)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(A	)

def _SCREAMING_SNAKE_CASE				(A = None  ,			A = None	)	->	Tuple:






       """simple docstring"""







       if rng_type == RNGType.TORCH:
              lowercase__				      =			torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              lowercase__				      =			torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              lowercase__				      =			torch.tensor(xm.get_rng_state()	)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              lowercase__				      =			torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              lowercase__				      =			torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              lowercase__				      =			generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       lowercase__				      =			AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              lowercase__				      =			rng_state.to(xm.xla_device()	)
              xm.collective_broadcast([rng_state]	)
              xm.mark_step()
              lowercase__				      =			rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              lowercase__				      =			rng_state.to(state.device	)
              torch.distributed.broadcast(A  ,			0	)
              lowercase__				      =			rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(A  ,			0	)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(A	)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(A	)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(A	)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(A	)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()	)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(A	)

def _SCREAMING_SNAKE_CASE				(A  ,			A = None	)	->	int:






       """simple docstring"""







       for rng_type in rng_types:
              synchronize_rng_state(RNGType(A	)  ,			generator=A	)




