


__snake_case             =	[
    ['''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		(		_lowercase   )				->   Dict:


     """simple docstring"""


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






def 		_A		(		_lowercase				,     _lowercase   )				->   Tuple:


     """simple docstring"""


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






def 		_A		(		_lowercase   )				->   Tuple:


     """simple docstring"""


     __UpperCamelCase   = get_distinct_edge(_lowercase   )
     __UpperCamelCase   = {}

     for item in distinct_edge:
          __UpperCamelCase   = get_bitcode(_lowercase				,     _lowercase   )
          # 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 _lowercase   : v[1][0]				,     reverse=_lowercase   )
     ]
     return sorted_frequency_table






def 		_A		(		_lowercase   )				->   Dict:


     """simple docstring"""


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






def 		_A		(		_lowercase   )				->   str:


     """simple docstring"""


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






def 		_A		(		_lowercase   )				->   Tuple:


     """simple docstring"""


     return [i * 1_00 / len(_lowercase   ) for i in cluster]






def 		_A		(		)				->   None:


     """simple docstring"""


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






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase   )				->   str:


     """simple docstring"""


     for i in cluster[ca]:
          __UpperCamelCase   = 0
          __UpperCamelCase   = ca + 1
          while ca < max(cluster.keys()   ):
               for j in cluster[ca]:
                    if int(_lowercase				,     2   ) & int(_lowercase				,     2   ) == int(_lowercase				,     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		(		_lowercase				,     _lowercase   )				->   List[str]:


     """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(_lowercase   ) - 1:
          create_edge(_lowercase				,     _lowercase				,     _lowercase				,     _lowercase   )
          __UpperCamelCase   = i + 1
     return graph






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase=None   )				->   Tuple:


     """simple docstring"""


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






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   Any:


     """simple docstring"""


     __UpperCamelCase   = int(s / 1_00 * (len(_lowercase   ) - 1)   )
     for i in cluster[k]:
          my_dfs(_lowercase				,     tuple(cluster[k][i]   )				,     (['Header'],)   )






def 		_A		(		_lowercase   )				->   Optional[Any]:


     """simple docstring"""


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






def 		_A		(		_lowercase   )				->   Union[str, Any]:


     """simple docstring"""


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


if __name__ == "__main__":
    preprocess(edge_array)
    __snake_case             =	get_frequency_table(edge_array)
    __snake_case             =	get_nodes(frequency_table)
    __snake_case             =	get_cluster(nodes)
    __snake_case             =	get_support(cluster)
    __snake_case             =	construct_graph(cluster, nodes)
    find_freq_subgraph_given_support(6_0, cluster, graph)
    __snake_case             =	[]
    __snake_case             =	freq_subgraphs_edge_list(paths)
    print_all()






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor



class   __lowerCamelCase (unittest.TestCase	):







       def __init__( self:   List[str],A_:   List[Any],A_:   List[Any]=7,A_:   str=3,A_:   Tuple=18,A_:   Union[str, Any]=30,A_:   Union[str, Any]=400,A_:   List[str]=True,A_:   List[Any]=None,A_:   Tuple=True,A_:   Union[str, Any]=None,A_:   int=True,A_:   Any=[0.5, 0.5, 0.5],A_:   Optional[Any]=[0.5, 0.5, 0.5],):






            '''simple docstring'''




            __UpperCamelCase   = size if size is not None else {'height': 20, 'width': 20}
            __UpperCamelCase   = crop_size if crop_size is not None else {'height': 18, 'width': 18}

            __UpperCamelCase   = parent
            __UpperCamelCase   = batch_size
            __UpperCamelCase   = num_channels
            __UpperCamelCase   = image_size
            __UpperCamelCase   = min_resolution
            __UpperCamelCase   = max_resolution
            __UpperCamelCase   = do_resize
            __UpperCamelCase   = size
            __UpperCamelCase   = do_center_crop
            __UpperCamelCase   = crop_size
            __UpperCamelCase   = do_normalize
            __UpperCamelCase   = image_mean
            __UpperCamelCase   = image_std







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }



@require_torch
@require_vision
class   __lowerCamelCase (_a							,			unittest.TestCase	):
       _lowercase       							=    DeiTImageProcessor if is_vision_available() else None
       _lowercase       							=    True







       def 		snake_case_  ( self:   List[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = DeiTImageProcessingTester(self				)







       @property
       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()







       def 		snake_case_  ( self:   Union[str, Any]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            self.assertTrue(hasattr(A_,'do_resize'				)				)
            self.assertTrue(hasattr(A_,'size'				)				)
            self.assertTrue(hasattr(A_,'do_center_crop'				)				)
            self.assertTrue(hasattr(A_,'center_crop'				)				)
            self.assertTrue(hasattr(A_,'do_normalize'				)				)
            self.assertTrue(hasattr(A_,'image_mean'				)				)
            self.assertTrue(hasattr(A_,'image_std'				)				)







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict				)
            self.assertEqual(image_processor.size,{'height': 20, 'width': 20}				)
            self.assertEqual(image_processor.crop_size,{'height': 18, 'width': 18}				)

            __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict,size=42,crop_size=84				)
            self.assertEqual(image_processor.size,{'height': 42, 'width': 42}				)
            self.assertEqual(image_processor.crop_size,{'height': 84, 'width': 84}				)







       def 		snake_case_  ( self:   Tuple				):






            '''simple docstring'''




            pass







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random PIL images
            __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_				)
            for image in image_inputs:
                 self.assertIsInstance(A_,Image.Image				)

            # Test not batched input
            __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random numpy tensors
            __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,numpify=A_				)
            for image in image_inputs:
                 self.assertIsInstance(A_,np.ndarray				)

            # Test not batched input
            __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)







       def 		snake_case_  ( self:   Any				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random PyTorch tensors
            __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,torchify=A_				)
            for image in image_inputs:
                 self.assertIsInstance(A_,torch.Tensor				)

            # Test not batched input
            __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)


