




__a			:	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  a_		(							__snake_case					)	->			Dict:

			'''simple docstring'''


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




def  a_		(							__snake_case     ,		__snake_case					)	->			str:

			'''simple docstring'''


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




def  a_		(							__snake_case					)	->			List[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= get_distinct_edge(__snake_case					)
			UpperCamelCase_     				= {}

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




def  a_		(							__snake_case					)	->			Union[str, Any]:

			'''simple docstring'''


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




def  a_		(							__snake_case					)	->			List[Any]:

			'''simple docstring'''


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




def  a_		(							__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			return [i * 1_0_0 / len(__snake_case					) for i in cluster]




def  a_		(							)	->			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  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


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




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


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




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case=None					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= (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  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Union[str, Any]:

			'''simple docstring'''


			UpperCamelCase_     				= int(s / 1_0_0 * (len(__snake_case					) - 1)					)
			for i in cluster[k]:
						my_dfs(__snake_case     ,		tuple(cluster[k][i]					)     ,		(['Header'],)					)




def  a_		(							__snake_case					)	->			Any:

			'''simple docstring'''


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




def  a_		(							__snake_case					)	->			List[Any]:

			'''simple docstring'''


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


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






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case=None					)	->			str:

			'''simple docstring'''


			UpperCamelCase_     				= None
			# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
			if str(weight.dtype					) == "torch.bfloat16":
						# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
						UpperCamelCase_     				= weight.view(torch.intaa					)
						UpperCamelCase_     				= 'bfloat16'
			UpperCamelCase_     				= weight.cpu().numpy()
			UpperCamelCase_     				= os.path.join(__snake_case     ,		F'''{weight_name}.dat'''					)
			if index is not None:
						if dtype is None:
									UpperCamelCase_     				= str(array.dtype					)
						UpperCamelCase_     				= {'dtype': dtype, 'shape': list(array.shape					)}
			if array.ndim == 0:
						UpperCamelCase_     				= array[None]
			UpperCamelCase_     				= np.memmap(__snake_case     ,		dtype=array.dtype     ,		mode='w+'     ,		shape=array.shape					)
			UpperCamelCase_     				= array[:]
			file_array.flush()
			return index




def  a_		(							__snake_case     ,		__snake_case					)	->			Dict:

			'''simple docstring'''


			UpperCamelCase_     				= tuple(weight_info['shape']					)
			if shape == ():
						# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
						UpperCamelCase_     				= (1,)

			UpperCamelCase_     				= weight_info['dtype']
			if dtype == "bfloat16":
						# NumPy does not support bfloat16 so this was saved as a int16
						UpperCamelCase_     				= 'int16'

			UpperCamelCase_     				= np.memmap(__snake_case     ,		dtype=__snake_case     ,		shape=__snake_case     ,		mode='r'					)

			if len(weight_info['shape']					) == 0:
						UpperCamelCase_     				= weight[0]
			UpperCamelCase_     				= torch.tensor(__snake_case					)
			if weight_info["dtype"] == "bfloat16":
						UpperCamelCase_     				= weight.view(torch.bfloataa					)

			return weight




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			if index is None or len(__snake_case					) == 0:
						# Nothing to save
						return

			UpperCamelCase_     				= os.path.join(__snake_case     ,		'index.json'					)
			if os.path.isfile(__snake_case					):
						with open(__snake_case     ,		'r'     ,		encoding='utf-8'					) as f:
									UpperCamelCase_     				= json.load(__snake_case					)
			else:
						UpperCamelCase_     				= {}
			current_index.update(__snake_case					)

			with open(__snake_case     ,		'w'     ,		encoding='utf-8'					) as f:
						json.dump(__snake_case     ,		__snake_case     ,		indent=2					)




def  a_		(							__snake_case     ,		__snake_case					)	->			Tuple:

			'''simple docstring'''


			os.makedirs(__snake_case     ,		exist_ok=__snake_case					)
			UpperCamelCase_     				= {}
			for name, parameter in state_dict.items():
						UpperCamelCase_     				= offload_weight(__snake_case     ,		__snake_case     ,		__snake_case     ,		index=__snake_case					)

			# Update index
			save_offload_index(__snake_case     ,		__snake_case					)




class     A					(				lowerCamelCase_       ):







							def __init__(    self   :	int   ,     __UpperCAmelCase   :	Mapping   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= dataset
										UpperCamelCase_     				= prefix







							def __getitem__(    self   :	List[Any]   ,     __UpperCAmelCase   :	Dict				)		->					Tuple:

										"""simple docstring"""



										return self.dataset[f'''{self.prefix}{key}''']







							def __iter__(    self   :	int				)		->					List[str]:

										"""simple docstring"""



										return iter([key for key in self.dataset if key.startswith(self.prefix				)]				)







							def __len__(    self   :	Any				)		->					Optional[Any]:

										"""simple docstring"""



										return len(self.dataset				)







class     A					(				lowerCamelCase_       ):







							def __init__(    self   :	int   ,     __UpperCAmelCase   :	Dict[str, torch.Tensor] = None   ,     __UpperCAmelCase   :	Optional[Union[str, os.PathLike]] = None   ,     __UpperCAmelCase   :	Mapping = None   ,     __UpperCAmelCase   :	Optional[int]=None   ,     )		->					List[str]:

										"""simple docstring"""



										if state_dict is None and save_folder is None:
													raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'				)

										UpperCamelCase_     				= {} if state_dict is None else state_dict
										UpperCamelCase_     				= save_folder
										if index is None and save_folder is not None:
													with open(os.path.join(__UpperCAmelCase   ,     'index.json'				)				) as f:
																UpperCamelCase_     				= json.load(__UpperCAmelCase				)
										UpperCamelCase_     				= {} if index is None else index
										UpperCamelCase_     				= list(self.state_dict.keys()				)
										self.all_keys.extend([key for key in self.index if key not in self.all_keys]				)
										UpperCamelCase_     				= device







							def __getitem__(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""simple docstring"""



										if key in self.state_dict:
													return self.state_dict[key]
										UpperCamelCase_     				= self.index[key]
										if weight_info.get('safetensors_file'				) is not None:
													if not is_safetensors_available():
																raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'				)

													from safetensors import safe_open

													UpperCamelCase_     				= 'cpu' if self.device is None else self.device
													with safe_open(weight_info['safetensors_file']   ,     framework='pt'   ,     device=__UpperCAmelCase				) as f:
																UpperCamelCase_     				= f.get_tensor(weight_info.get('weight_name'   ,     __UpperCAmelCase				)				)

													if "dtype" in weight_info:
																return tensor.to(getattr(__UpperCAmelCase   ,     weight_info['dtype']				)				)
													else:
																return tensor

										UpperCamelCase_     				= os.path.join(self.save_folder   ,     f'''{key}.dat'''				)
										return load_offloaded_weight(__UpperCAmelCase   ,     __UpperCAmelCase				)







							def __iter__(    self   :	Optional[int]				)		->					Union[str, Any]:

										"""simple docstring"""



										return iter(self.all_keys				)







							def __len__(    self   :	Any				)		->					Any:

										"""simple docstring"""



										return len(self.all_keys				)







def  a_		(							__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= {}
			for module_name in submodule_names:
						# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
						# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
						result.update(
						    {
						        key: param
						        for key, param in state_dict.items()
						        if key == module_name or key.startswith(module_name + '.'					)
						    }					)
			return result
