"""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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any						)    ->     int:







     '''simple docstring'''


     __A					:					Union[str, Any]								=		set()
     for row in edge_array:
          for item in row:
               distinct_edge.add(item[0]						)
     return list(_SCREAMING_SNAKE_CASE						)




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







     '''simple docstring'''


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




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







     '''simple docstring'''


     __A					:					List[str]								=		get_distinct_edge(_SCREAMING_SNAKE_CASE						)
     __A					:					Any								=		{}

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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Dict						)    ->     str:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple						)    ->     str:







     '''simple docstring'''


     __A					:					int								=		{}
     for key, value in nodes.items():
          __A					:					List[str]								=		value
     return cluster




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				str						)    ->     Dict:







     '''simple docstring'''


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




def 						_lowercase				(  )    ->     None:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Dict						)    ->     int:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				int						)    ->     Tuple:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[Any]  ,       _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       _SCREAMING_SNAKE_CASE				:				str=None						)    ->     Tuple:







     '''simple docstring'''


     __A					:					Union[str, Any]								=		(path or []) + [start]
     if start == end:
          paths.append(_SCREAMING_SNAKE_CASE						)
     for node in graph[start]:
          if tuple(_SCREAMING_SNAKE_CASE						) not in path:
               my_dfs(_SCREAMING_SNAKE_CASE  ,       tuple(_SCREAMING_SNAKE_CASE						)  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				int  ,       _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				List[Any]						)    ->     Optional[int]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Dict						)    ->     List[Any]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any						)    ->     Dict:







     '''simple docstring'''


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


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




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()					,       reason='XFormers attention is only available with CUDA and `xformers` installed'					,       )
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,       )



